Dev Shorts

[Dev Catch Up # 108] - GPT 5.5, DeepSeek V4, Claude Design, Kimi K2.6, ChatGPT Images 2.0, Qwen3.6 27B, HyperFrames, OpenAI's Privacy Filter, Parallel's Web Search MCP Server and much more!

Aravind Putrevu — Fri, 24 Apr 2026 13:17:44 GMT

Welcome to the 108th edition of DevShorts, Dev Catch Up.

For those who joined recently or are reading Dev Catch Up for the first time, I write about developer stories and open source, partly based on my work and experience interacting with people all over the globe.

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Must Read

OpenAI has released GPT 5.5, their smartest model so far. It is good at coding, debugging, and creating docs. It is also strong at agentic coding, computer use, and research work. OpenAI says it matches GPT 5.4 speed while completing Codex tasks with fewer tokens. Check OpenAI’s announcement for more details.
DeepSeek has released DeepSeek V4. The model is open sourced and comes with 1M context window at a lower cost. It has two versions, Pro for stronger performance and Flash for faster and economical use. You can try it on Chat or use the API. Check DeepSeek’s post for more details.
Anthropic has launched Claude Design, a new tool for creating visual work with Claude. It can help with wireframes, prototypes, pitch decks, landing pages, and campaign visuals. It can also build visuals that match your existing brand style. Check Anthropic’s announcement for more details.
Moonshot AI has released Kimi K2.6, an open source coding model. It is built for long running coding tasks. It can also create richer frontend like animated hero sections and motion based UI. It also supports large agentic workflows. Check Kimi’s post for more details.

OSS Highlight of the Week

This week we are featuring HyperFrames. It is an open source video rendering framework. It lets you create videos using HTML, preview them in the browser, and export them as MP4. It also works well with AI coding agents like Claude Code, Cursor, Gemini CLI, and Codex, so they can help you create videos with animations. Check the GitHub repo for more details.

Anthropic is testing a change to Claude Code access. Claude Code access was removed from the Pro plan for around 2% of new Pro signups. Those users now need the $100 Max plan to use Claude Code. Existing Pro users are not affected for now, but we need to wait and see if this expands later.
Anthropic’s Mythos model was reportedly accessed by a private Discord group. Mythos was meant only for selected partners, because Anthropic considered it too powerful for public release. The group reportedly used leaked vendor details to find the access path. Check Bloomberg’s report for more details.
Vercel reported a recent security incident. It started after Context.ai, a third party AI tool used by one employee, was compromised. The attacker used that access to enter some Vercel systems. They also decrypted some non sensitive environment variables. Read Vercel’s security bulletin for full details.
Cursor and SpaceXAI are working together on AI for coding tasks. Cursor brings the coding product. SpaceXAI brings the Colossus training supercomputer. SpaceXAI also has the option to acquire Cursor later this year for $60 billion. Check the post for more details.

Notable FYIs

OpenAI has released ChatGPT Images 2.0. It improves image generation, text rendering, and multilingual support. It can create visuals for social posts, presentations, infographics, comics, and other content. Check OpenAI’s announcement for more details.
Qwen has released Qwen3.6 27B, a 27B dense model focused on coding. It delivers strong agentic coding performance in a much smaller model. It also supports multimodal input and can work with coding agents. Check Qwen’s blog for more details.
OpenAI has released Privacy Filter, an open source model for finding and masking personal data in text. It is useful for teams that want to clean sensitive data on their own systems. It can redact PII before the data is stored or processed. Check the GitHub repo for more details.
We now have a free web search MCP server from Parallel. It is used for web search inside AI agents and MCP supported tools. It does not need any account or API key. You can use it with Claude Code, Codex, or any MCP supported tool. Check Parallel’s post for more details.

That’s it from us with this edition. We hope you are going away with a ton of new information. Lastly, share this newsletter with your colleagues and pals if you find it valuable. A subscription to the newsletter will be awesome if you are reading it for the first time.

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[Dev Catch Up # 107] - Claude Opus 4.7, GPT 5.4 Cyber, Gemini 3.1 Flash TTS, Windsurf 2.0 with Devin, Qwen3.6-35B MOE Model, Routines in CC, GPT Rosalind, Gemini for Mac, Codex for everything & more!

Aravind Putrevu — Fri, 17 Apr 2026 12:31:55 GMT

Welcome to the 107th edition of DevShorts, Dev Catch Up.

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Must Read

Anthropic has released Claude Opus 4.7. It comes with stronger coding, high resolution vision, and a new xhigh effort level. Pricing remains the same as Opus 4.6. Anthropic notes that Mythos Preview still remains its best aligned model based on its evaluations. Check Anthropic’s page for more details.
Google has released Gemini 3.1 Flash TTS. It generates more natural speech and supports 70+ languages. It also adds audio tags to control vocal style, pace, and delivery. Its audio output is watermarked with SynthID to identify AI generated content. Check Google’s announcement for more details.
OpenAI has rolled out GPT 5.4 Cyber. It is built for defensive security work. Access to this model is now open through identity verification. OpenAI says it can inspect compiled software and find malware, vulnerabilities, and other security issues without the original source code. Check OpenAI’s post for details.
Qwen has open sourced Qwen3.6 35B A3B. It is a sparse MoE model with 35B total parameters and 3B active. It comes with strong agentic coding and multimodal reasoning. It also supports both thinking and non thinking modes in one model. Check Qwen’s announcement for more details.

OSS Highlight of the Week

This week we are featuring Scion. It is an open source multi agent orchestration tool. It runs each agent in its own container and git worktree. It helps multiple agents work in parallel without getting in each other’s way. It works with Claude Code, Gemini CLI, Codex, and others too. Check the GitHub repo for more details.

Google has launched the Gemini app for Mac. It is a native macOS app. You can share anything on your screen with Gemini to get help, including local files. Check Google’s post for more details.
OpenAI has introduced GPT Rosalind. It is a new model built for life sciences research and drug discovery. OpenAI says it can reason across chemistry, proteins, and DNA, and help with experiment analysis. Check OpenAI’s post for more details.
Google has solved one of the old problems in RNNs with Memory Caching. Earlier, RNNs had to rely on one fixed memory as the input got longer. This new method keeps useful memory from earlier in the sequence. The paper says this improves long context and recall heavy tasks. It also brings RNNs closer to Transformers. Check the paper for more details.
I came across Baileys. It is a WebSockets-based TypeScript library for interacting with the WhatsApp Web API. If you are working on WhatsApp based apps, this is worth a look. Check the GitHub repo for more details.

Notable FYIs

Anthropic has introduced routines in Claude Code. You can now set up repeatable automations and run them on a schedule, through an API call, or from GitHub events. Since they run on Claude Code’s web infrastructure, you do not need to keep your laptop open. Check Anthropic’s post for more details.
Windsurf has launched Windsurf 2.0. It adds a new Agent Command Center to manage local and cloud agents in one place. Devin is now built into Windsurf, so cloud agents can keep working even after you close your laptop. Check Windsurf’s post for more details.
OpenAI has updated Codex in a big way. It now goes beyond coding. It can handle computer use, run multiple agents, and browse the web. It also adds image generation and 90+ new plugins. OpenAI is also adding memory and automations for long running work. Check OpenAI’s post for more details.
Google has introduced Skills in Chrome. You can now turn your prompts into one click tools. They can run on the page you are viewing or across selected tabs. Google is also adding a ready made Skills library. Check Google’s post for more details.
If you are building agents, this is a must read. Garry Tan shared a useful idea on building AI agents. He says keep the harness thin and put more value into skills. His point is that the real bottleneck is not model intelligence. It is whether the model understands your data, process, and context. Check Garry Tan’s post for more details.

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[Dev Catch Up # 106] - Anthropic's Mythos, Meta's Muse Spark, Amazon's S3 Files, llmfit, OpenClaw's Dreaming feature,Claude Managed Agents,Graphify, nono-sandbox,Perplexity's plaid integration & more!

Aravind Putrevu — Fri, 10 Apr 2026 13:03:43 GMT

Welcome to the 106th edition of DevShorts, Dev Catch Up.

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Must Read

Anthropic’s new Project Glasswing is getting a lot of attention. It is a new effort to secure critical software. It is built around Claude Mythos Preview, Anthropic’s latest model. Anthropic says Mythos can find serious software flaws. It has already identified flaws in the Linux kernel and major browsers. Because of its capabilities, Anthropic is not making it publicly available. Check Anthropic’s announcement for more details.
Meta is back in the model race with Muse Spark. It is the first model from Meta Superintelligence Labs. It supports multimodal reasoning and visual chain of thought, and multi agent tasks. Check Meta’s announcement for more details.
Amazon has announced S3 Files. It lets you access S3 buckets like a file system, without moving data out of S3. It gives file system access to S3 data with low latency. Check AWS’s announcement for more details.
OpenClaw has released a new memory feature called Dreaming. It is a background memory consolidation system for long term recall. It takes useful short term context and stores it in durable memory. It is off by default. Check OpenClaw’s docs for more details.

OSS Highlight of the Week

This week we are featuring nono. It is an open source sandbox for AI agents. It lets you run agents in an isolated environment without extra infrastructure. It also supports tools like Claude Code, Codex, OpenCode, and OpenClaw. Check the nono GitHub repo for more details.

Anthropic has launched Claude Managed Agents. It helps teams build and run cloud hosted agents on Anthropic’s infrastructure. Anthropic handles things like sandboxing, permissions, state, and tracing. So teams can focus more on the agent itself. Check Anthropic’s announcement for more details.
LangChain has introduced Deep Agents Deploy. It is an open alternative to Claude Managed Agents. It helps teams deploy production ready agents with one command. It is model agnostic and open source, so teams are not locked into one provider. Check LangChain’s announcement for more details.
If you keep a lot of notes in markdown, Atomic is worth a look. It is an open source personal knowledge base. It turns markdown notes into a connected knowledge graph. You can explore it through wiki style pages, a canvas, and chat. Check the Atomic GitHub repo for more details.
xAI looks to be scaling up fast. Elon Musk says they have 7 models in training. They include Imagine V2, two 1T variants, two 1.5T variants, a 6T model, and a 10T model. Check Elon Musk’s post for more details.
IBM Research has released ALTK Evolve for AI agents. It helps agents learn from past runs instead of just re reading old transcripts. IBM says it turns agent traces into reusable guidelines, and improves reliability on harder tasks. Check the Hugging Face post for more details.
Microsoft has released Memento. It is a new way to extend LLM output length without needing a bigger context window. It breaks reasoning into blocks, writes short summaries, and clears old KV cache. So the model can keep going longer. Check the Memento GitHub repo for more details.

Notable FYIs

llmfit looks useful if you run models locally. It checks your RAM, CPU, and GPU, then tells you which models will run well on your machine. It also scores them across fit, speed, quality, and context. It comes with both a terminal UI and a CLI mode. Check the llmfit GitHub repo for more details.
The skill called Graphify caught my attention. It is an open source skill for Claude Code and other coding assistants. It turns a folder of code, PDFs, markdown, and images into a queryable knowledge graph with one command. It can help you understand a codebase faster and trace architecture decisions. Check the Graphify GitHub repo for more details.
Claude Code has added /autofix-pr in the CLI. You can now trigger PR autofix directly after finishing your PR. It sends your session to the cloud, so the autofixer gets full context to handle CI failures and review comments. Check the X post for more details.
Perplexity has added Plaid integration to its Computer agent. Users can now link bank accounts, credit cards, and loans for a fuller view of their finances. Perplexity says Computer can analyze spending, track net worth, and build custom finance tools from simple prompts. Check Perplexity’s announcement for more details.

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[Dev Catch Up # 105] - Gemma 4, Qwen3.6 Plus, Microsoft's MAI Models, GLM 5V Turbo, Semiotic, Claude Code's Source Code Leaked, Claude Code tips, Video chat with your agents with this Skill & more!

Aravind Putrevu — Fri, 03 Apr 2026 13:31:27 GMT

Welcome to the 105th edition of DevShorts, Dev Catch Up.

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Must Read

Google has released Gemma 4. It is a new open model family. It is built from the same research and technology behind Gemini 3. Google says it supports agent workflows, multimodal reasoning, and 140 languages. Check Google DeepMind’s post for more details
Microsoft has announced three new MAI models in Foundry. They are MAI Transcribe 1, MAI Voice 1, and MAI Image 2. These cover speech to text, voice generation, and image generation. The models focus on speed, quality, and lower cost. Check Microsoft’s announcement for more details.
Qwen has introduced Qwen3.6 Plus. It comes with better coding, stronger multimodal vision, and a 1M context window in the API. Qwen says it is built to support real world agents and developer workflows. Check Qwen’s blog for more details.
Anthropic accidentally exposed Claude Code source code through a map file in its npm package. The leaked code then spread across GitHub through multiple repos and forks. Anthropic sent takedown notices to remove those leaked copies. Check the TechCrunch report for more details.

OSS Highlight of the Week

This week we are featuring Semiotic. It is a React library for building data visualizations in web apps. It comes with schemas and an MCP server, so AI coding assistants can generate correct chart code on the first try. Check the GitHub repo for more details.

Claude Code now has a No Flicker mode. It reduces screen flicker and keeps memory usage stable in long chats. It also adds mouse support in the terminal. Check Anthropic’s announcement for more details.
Prism ML has introduced 1 bit Bonsai 8B. It is a model with 1 bit weights and needs only 1.15 GB of memory. Prism says it is built for robotics, real time agents, and edge use cases. Check Prism ML’s announcement for more details.
Anthropic has shared new research on emotion concepts in language models. The research says models can form internal emotion related representations from human text. Anthropic says these patterns can influence how Claude behaves in some cases. Check Anthropic’s research post for more details.
AI has helped a solo founder build a $1.8B business. Medvi is one reported example built with AI tools for coding, ads, and support. It shows how a very small team can now scale much faster with AI. Check the New York Times report for more details.
Andrej Karpathy shared a workflow for building personal knowledge bases with LLMs. He uses them to turn papers, articles, repos, and images into a markdown wiki. The wiki then keeps growing through search, Q and A, and new outputs. Check Karpathy’s post for more details.

Notable FYIs

Pika has released a beta video chat skill for agents. It is powered by a new real time model called PikaStream 1.0. Pika says it keeps memory and personality during calls and can also support agentic tasks in the same session. Check Pika’s announcement for more details.
Boris Cherny shared practical Claude Code tips from his daily workflow. He showed features like teleport, remote control, loop, schedule, hooks, and Dispatch. These help Claude Code run across devices and automate more work. Check Boris Cherny’s thread for more details.
Qwen has released Qwen3.5 Omni. It is a native multimodal model that can understand text, images, audio, and video. Qwen says it brings stronger multilingual support and better audio and video understanding. Check Qwen’s blog for more details
Falcon has released Falcon Perception. It is a vision model for referring expression segmentation. Along with it, they also released Falcon OCR, a 0.3B OCR model. Falcon says it performs on par with much larger models. Check the Falcon’s post for more details.
Z.ai has introduced GLM 5V Turbo, a vision model built for coding. It can understand images, video, text, and files as input. Z.ai says it is built for multimodal coding tasks and can work with agents like OpenClaw. Check Z.ai’s docs for more details

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[Dev Catch Up # 104] - Gemini 3.1 Flash Live, Claude's Computer Use, SORA gets shut down, HF hf-mount, TurboQuant, CC Cheat Sheet, Email.md, Auto-Mode in CC, LiteLLM Security Incident,MiroFish & more!

Aravind Putrevu — Fri, 27 Mar 2026 12:36:21 GMT

Welcome to the 104th edition of DevShorts, Dev Catch Up.

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Must Read

Google has introduced Gemini 3.1 Flash Live, its latest real time audio model. It is built for more natural voice conversations and better task handling. Google is rolling it out across more of its products. Check Google’s announcement for more details.
Google Research has introduced TurboQuant. Its a new compression method for AI models and vector search systems. It is designed to shrink memory use without hurting model accuracy. Google says TurboQuant can cut KV memory by at least 6 times. Check Google Research’s post for more details.
Hugging Face has introduced hf mount. It allows you attach Hub storage, models, and datasets like a local file system. You can now attach a remote storage that is 100x bigger than your local disk. It may be useful for Agentic Storage. Check Hugging Face’s post for more details.
LiteLLM was recently compromised in a suspected supply chain incident. The team has shared a detailed report and the affected versions. It is worth checking if you use LiteLLM in production. Check LiteLLM’s security update for more details.

OSS Highlight of the Week

This week’s OSS highlight is emailmd. It lets you write emails in Markdown and convert them into email ready HTML. It helps to create responsive emails without writing complex HTML. Check the GitHub repo for more details.

Good to know

OpenAI is reportedly shutting down Sora, its AI video product. They do this to free up compute for its next major model called Spud. This shows OpenAI is shifting focus back to its next big model release. Check the latest reports for more details.
Anthropic has added a new computer use feature to Claude. It can operate your Mac and complete real tasks. It can open apps, browse, and do desktop actions for you. It is now in research preview inside Claude Cowork and Claude Code on macOS. Check Anthropic’s post for more details.
Cloudflare has launched Dynamic Worker Loader. It lets developers run AI code in isolated sandboxes on demand. It makes agent execution much faster and more memory efficient than containers. Check Cloudflare’s post for more details.
Anthropic has introduced auto mode in Claude Code to reduce constant permission prompts. It can decide some file writes on its own. The goal is to reduce approval fatigue while maintaining safety controls. Check Anthropic’s post for more details.

Notable FYIs

Mistral has introduced Voxtral TTS, its first text to speech model. It is built for multilingual voice generation, low latency, and custom voice support. It is available through API, Mistral Studio and Hugging Face. Check Mistral’s announcement for more details.
MiroFish is an AI prediction engine built on multi agent technology. It takes real world signals like breaking news, policy drafts, or financial data and builds a parallel digital world for simulation. So teams can test possible future scenarios in a digital sandbox. Check the GitHub repo for more details.
Luma has introduced UNI 1, a new multimodal model for image generation. It is built for better image understanding, editing, and reference based generation. Luma says it performs well across image quality. Its API access is coming soon. Check Luma’s UNI 1 page for more details.
There is a Claude Code cheat sheet for quick reference. It brings shortcuts, slash commands, MCP options, and recent updates into one place. It is useful if you use Claude Code often and want everything in one view. Check it out for more details.
Mozilla AI has released cq, a new open standard for shared agent learning. It lets agents store, share, and query past knowledge. So they do not keep repeating the same mistakes. It is worth a look if you are exploring agent memory and team level learning. Check the GitHub repo for more details.
Google has introduced Lyria 3 Pro. Its latest music generation model. It can create longer tracks of up to 3 minutes. It gives more control over parts like intros, verses, and choruses. This will be available in all google applications like Vertex AI, Gemini App. Check Google’s announcement for more details.

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[Dev Catch Up # 103] - GPT 5.4 mini and Nano, NVIDIA's NemoClaw, OpenAI's Model Challenge, Zeroboot, AutoResearchClaw, Google’s Stitch, Cowork's Dispatch, Unsloth Studio, Mistral Small 4 and more!

Aravind Putrevu — Fri, 20 Mar 2026 13:37:44 GMT

Welcome to the 103rd edition of DevShorts, Dev Catch Up.

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Must Read

OpenAI has released GPT 5.4 mini and nano. GPT 5.4 mini is a smaller model with better coding, reasoning, multimodal understanding, and tool use. It is 2x faster than GPT 5 mini. GPT 5.4 nano is the cheaper option for smaller tasks. Check OpenAI’s announcement for more details.
NVIDIA has released NemoClaw. It is an open source setup that helps you run OpenClaw assistants in a safer way. It sets up NVIDIA OpenShell for secure agent runs and supports open models like NVIDIA Nemotron. Check the NemoClaw GitHub repo for more details.
MiniMax has released M2.7. It can handle project delivery, bug fixing, log analysis, and office tasks like Word, Excel, and PPT. It is also the company’s first model to take part in its own evolution. Check MiniMax’s announcement for more details.
Manus has introduced My Computer. Its a Desktop app. It works with local files, folders, tools, and apps. It can run terminal commands, edit files, and handle desktop tasks with your approval. It is available now for macOS and Windows users. Check Manus’s announcement for more details

OSS Highlight of the Week

This week’s OSS highlight is AutoResearchClaw. It is an open source research agent. It turns a research idea into a full paper. It can search papers from OpenAlex, Semantic Scholar, and arXiv and generate experiments, charts, reviews, and conference ready LaTeX output. It is useful for developers and researchers who want to test ideas faster. Check the GitHub repo for more details.

Anthropic is shipping Dispatch in Claude Cowork as a research preview. It gives you one persistent conversation with Claude that runs on your computer. You can message it from your phone and come back later to finished work. To try it, download Claude Desktop and pair your phone.
Unsloth has introduced Unsloth Studio. It is an open source web UI for working with open models locally. You can run models, train them, and export them from one place. It also supports files like PDF, CSV, JSON, and DOCX for training. Check Unsloth’s docs for more details.
Google has updated Stitch. It turns prompts into UI. It comes with new canvas, a design agent, voice support, and DESIGN.md for reusing design rules across tools. It also supports integrations through MCP and SDKs. Check Google’s Stitch announcement for more details.
Cursor has introduced Composer 2. Cursor says it performs better than earlier Composer versions on coding benchmarks. Check Cursor’s announcement for more details.

Notable FYIs

OpenAI has launched a Challenge called Parameter Golf. It asks people to train the best language model that fits in 16MB and trains in under 10 minutes on 8xH100s. OpenAI is also offering compute credits. The challenge runs from March 18 to April 30. Check OpenAI’s post for more info.
Zeroboot creates sandboxes for AI agents very fast. It can start VM sandboxes in less than a millisecond. The repo also includes SDKs and self hosting docs. It is still a prototype, so it is not ready for production yet. Check the Zeroboot GitHub repo for more details.
Microsoft has introduced MAI Image 2. It is their new image model built for creative work. It improves photorealism, image text generation, and detailed scene creation. It is available now in MAI Playground, Copilot and Bing Image Creator. Check Microsoft’s announcement for more details.
Z.AI has released GLM 5 Turbo. It is built for OpenClaw style agent tasks. It supports tool calling, long chain tasks, MCP, and a 200K context window. Check Z.AI’s docs for more details.
Mistral has released Mistral Small 4. It brings reasoning, coding, and image understanding into one system. It combines capabilities from Mistral’s earlier model families. It comes with a 256k context window and is open source. Check Mistral’s announcement and docs for more details.
World has introduced AgentKit. It adds human verification for agents. It is powered by x402 and built by Coinbase and Cloudflare. The goal is to make agent automation more trusted. Check World’s announcement and docs for more details.

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[Dev Catch Up # 102] - Gemini Embedding 2, Andrew Ng's Context Hub, Nemotron 3 Super, Perplexity's Personal Computer, gstack, AMI Labs, Agent 4, Karpathy's Auto Research, RCLI-Voice AI for Mac & more!

Aravind Putrevu — Fri, 13 Mar 2026 13:45:15 GMT

Welcome to the 102nd edition of DevShorts, Dev Catch Up.

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Must Read

Google has released Gemini Embedding 2, its first natively multimodal embedding model. It can map text, images, video, audio, and documents into one embedding space. This is useful for multimodal RAG, search, and retrieval use cases. Check Google’s Gemini Embedding 2 announcement for more details.
Andrew Ng has released Context Hub. It helps coding agents use curated and versioned docs through a CLI. It is designed to reduce API hallucinations and help agents improve across tasks. Check the Context Hub GitHub repo for more details.
NVIDIA has released Nemotron 3 Super, a new open 120B model for agentic AI. It is built for long and complex agent workflows. It supports a 1 million token context window and higher throughput. Check NVIDIA’s Nemotron 3 Super announcement for more details.
Perplexity has opened the waitlist for Personal Computer. It brings Perplexity Computer and Comet Assistant to your own system. Perplexity says sensitive actions need your approval and all actions are logged. Check Perplexity’s page for more details.

OSS Highlight of the Week

This week we are featuring RCLI. It is an on device voice AI for macOS. You can control macOS actions with voice and also query your documents with voice. No cloud and no API keys. Check the RCLI GitHub repo for more details.

Cloudflare has added a new /crawl endpoint in Browser Rendering. It lets developers crawl an entire website with one API call. It returns the content in HTML, Markdown, and structured JSON. Check Cloudflare’s post for more details.
Microsoft has introduced Copilot Cowork. It helps Copilot turn a request into a plan and take action. It works using signals from Microsoft 365 apps like Outlook, Teams, and Excel. Check Microsoft’s post for more details.
OpenAI has opened applications for Codex for Open Source. Selected maintainers get ChatGPT Pro with Codex, Codex Security, and API credits. Check OpenAI’s page for more details.
Anthropic has launched Claude Marketplace. It allows customers to buy Claude powered tools from partners like GitLab, Replit, and Snowflake using their Anthropic commitment. Check Anthropic’s page for more details.
OpenAI has added interactive learning for math and science in ChatGPT. Now you can learn concepts with visual explanations inside ChatGPT. This makes formulas and graphs easier to understand. Check OpenAI’s post for more details.

Notable FYIs

Ex Meta Chief Scientist Yann LeCun has launched AMI Labs. AMI Labs aims to solve the limits of standard language models by building world models. Its initial focus is on companies that run complex systems, like automotive, aerospace, biomedical, and pharma. Check the AMI Labs site for more details.
Y Combinator CEO Garry Tan has released gstack. It brings his Claude Code setup with 6 opinionated tools for roles like CEO, Engineering Manager, Release Manager, and QA Engineer. It is built to turn one coding agent into a team of specialist workflows. Check the gstack GitHub repo for more details.
Andrej Karpathy has released autoresearch. It lets AI agents run small LLM training experiments automatically on a single GPU. The agent edits the training code, runs a fixed 5 minute experiment, checks the result, and repeats. Check the autoresearch GitHub repo for more details.
Came across this X post where a techie shared how he hacked Perplexity Computer and got access to the Claude Code API key used by Perplexity. He also shared a safer pattern for handling keys in multi agent systems. Check the X post for more details.
Hume has open sourced TADA, a speech language model for text to speech. It is designed for faster generation and to reduce hallucinated words in speech output. This could be useful for teams building voice products. Check Hume’s post for more details.
Replit has introduced Agent 4 for more creative app building workflows. It can generate UI variants and handle multiple tasks in parallel. This helps developers move faster while building inside Replit. Check Replit’s Agent 4 announcement for more details

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[Dev Catch Up # 101] - GPT-5.4, Gemini 3.1 Flash Lite, Google Workspace CLI, import-memory in claude, Qwen 3.5 Small models, Voice Mode in Claude Code, Claude-Max for open-source maintainers and more!

Aravind Putrevu — Fri, 06 Mar 2026 13:03:40 GMT

Welcome to the 101st edition of DevShorts, Dev Catch Up.

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Some recent issues from Dev Catch up:

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Must Read

OpenAI has released GPT-5.4. It is their most capable model so far. They also released GPT-5.4 Pro in ChatGPT and the API for harder tasks and long running workflows. It brings improvements in reasoning, coding, and agentic workflows. Check OpenAI’s GPT-5.4 announcement for more details.
Many tools are now moving to the CLI, so agents can use them. Google Workspace released Workspace CLI. It lets you access Gmail, Drive, Docs, and Calendar directly from the terminal. The engineer who built it also wrote a post on designing CLIs for AI agents. Check the Google Workspace CLI GitHub repo for installation and usage details.
Google has released Gemini 3.1 Flash Lite, a lightweight model built for high volume workloads. It focuses on speed and lower cost while still handling complex tasks. It is available through the Gemini API, AI Studio, and Vertex AI. Check Google’s Gemini 3.1 Flash Lite announcement for more details.
Qwen introduced the Qwen 3.5 Small model series with 0.8B, 2B, 4B, and 9B sizes. These models aim to deliver stronger performance with lower compute. They also released matching Base models for research and fine tuning. Check Qwen’s Twitter post for more details.

OSS Highlight of the Week

This week we are featuring Hermes Agent, an open source AI agent from Nous Research. It runs on your machine. It acts as a personal agent and learns your projects. It builds skills over time. It can connect to tools like Slack, Telegram, and Discord and automate tasks on a schedule. Check the Hermes Agent GitHub repo to explore more.

Anthropic introduced Import Memory in Claude. Now you can bring context from other AI tools with a simple copy paste flow. This helps Claude pick up your preferences and project context faster. Check Claude’s Import Memory page for more details.
Cursor added MCP Apps. It allows interactive interfaces like charts, diagrams, and whiteboards to appear directly inside Cursor. This means tools can return rich visual interfaces instead of just text responses. Check Cursor’s MCP App’s post for more details.
A new interview with Boris Cherny, the creator of Claude Code, explains how the tool was built and how developers are using it today. It covers workflows like running parallel agents and more. Check the Pragmatic Engineer post for the full interview.
If you are building LLMs, this post is worth reading. It explains how open weight LLMs are built. It covers ideas like MoE models, active parameters, and training choices in a simple way. Check the full post for more details.
OpenAI is reportedly building a GitHub rival. The project is still in the early stages. It may be offered to OpenAI’s existing customers. The move comes after several GitHub outages that disrupted developer workflows in recent months. Check the report for more details.

Notable FYIs

OpenAI released Symphony. It turns project work into isolated implementation runs. It spawns agents to handle the tasks. The agents complete the work and provide proof of execution. This lets teams manage the work instead of supervising coding agents. Check OpenAI’s Symphony for more details.
The much awaited Voice Mode is rolling out in Claude Code. It lets developers talk to Claude directly instead of typing prompts. The feature is currently live for a small percentage of users and will roll out more broadly in the coming weeks. Check the post for early demos and rollout details
Anthropic offers 6 months of free Claude Max access for eligible open source maintainers and contributors. The goal is to support developers who maintain important OSS projects. Check the Claude for OSS page to see eligibility and application details.
Cursor introduced Automations. They can run on a schedule or based on events like Slack messages, GitHub PRs, Linear issues, and PagerDuty incidents. You can also configure your own custom events using webhooks. Check Cursor’s Automations post for more details.
This Substack post talks about AI tooling for software engineers in 2026. It shows which tools developers use the most. It also explains how common AI has become in daily development work. Check the Pragmatic Engineer post for the full breakdown.

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[Dev Catch Up # 100] - Perplexity Computer, Nano Banana 2, Claude Code Remote Control, ZeroClaw, WebSocket Mode in OpenAI API, Claude Opus 3 retirement, Plano, Memento and much more!

Aravind Putrevu — Fri, 27 Feb 2026 12:05:59 GMT

Welcome to the 100th edition of DevShorts, Dev Catch Up.

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Some recent issues from Dev Catch up:

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Must Read

Google has released Nano Banana 2, a new image model that brings Nano Banana Pro quality at Gemini Flash speed. It improves lighting, textures, and details while keeping Flash level speed. It is rolling out across Google’s Gemini platforms. Check Google’s announcement for details.
Perplexity has introduced Perplexity Computer. It is a general purpose digital worker. It can research, build, deploy, and manage a project from start to finish. It runs in the same chat interface. Each task runs in its own sandbox. It uses 19 different models for tasks. Check Perplexity’s announcement for details.
Anthropic has released Remote Control for Claude Code. It lets you start a terminal task on your computer and continue it from your phone. You link devices by scanning a QR code in the terminal. It is now available for Max plans only. Check Anthropic’s announcement for details.
OpenAI has added WebSocket mode for the Responses API. This helps long agent workflows, especially with many tool calls. It keeps the connection open. Each time, you just send the new input and the id of the last response. Check OpenAI’s docs for more details.

OSS Highlight of the Week

This week we are featuring ZeroClaw. It is a runtime operating system for agentic workflows. It abstracts models, tools, memory, and execution so agents can be built once and run anywhere. It can run on low cost hardware. It is far lighter than many other agent runtimes. Check the ZeroClaw GitHub repo for details.

Anthropic is keeping Claude Opus 3 available even after retirement. Paid users can still access it, and API access is available by request. Opus 3 will also continue writing weekly essays in Claude’s Corner for the next three months. The essays will be reviewed before publishing, but not edited. Check Anthropic’s announcement for details.
Google is deprecating Gemini 3 Pro Preview on Gemini API and Google AI Studio on March 9, 2026. If you are using the latest alias, it will switch to Gemini 3.1 Pro Preview. Check your integrations and migrate in time to avoid service disruption.
Perplexity has partnered with Samsung to bring Perplexity into the upcoming Galaxy S26. It will be built in as a system level AI with its own wake word, “Hey Plex.” Check Perplexity’s announcement for details.
We have a new auto memory feature in Claude. Claude can now remember useful context like debugging patterns and preferred approaches across chats. So we do not have to repeat the same details every time. Check the twitter announcement for details.

Notable FYIs

Anthropic has added Scheduled Tasks to Cowork. Claude can now run recurring tasks at specific times, like a morning brief, weekly sheet updates, or Friday presentations. Check Anthropic’s announcement on Scheduled Tasks for details.
If you do web scraping, Scrapling is worth a look. It is a Python framework that adapts to site changes. It helps handle anti bot pages. It can scale from one request to full crawls. Check the Scrapling GitHub repo for details.
If you work with agents, you know skills matter. Antigravity Awesome Skills is a community repo with ready to use skills for Claude Code, Antigravity, Cursor, and more. Review each skill before you install it on your agent.
We build Agentic apps. But shipping them is not easy. Plano is a model native proxy server and a universal data plane for Agentic apps. It handles routing, guardrails, and observability, so you can focus on the agent logic. Check the Plano GitHub repo for details.
It is not always necessary to fine tune Agents to improve performance. Memento is a framework that helps agents learn. It saves what worked and what failed. It then uses that memory on new tasks. It follows a planner and executor loop. Check the Memento GitHub repo for details.

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[Dev Catch Up # 99] - Gemini 3.1 Pro, Claude Sonnet 4.6, Qwen 3.5, Grok 4.2, How Codex is built, microgpt, Code Wiki, Manus Agents, Excalidraw MCP Server, Entire CLI, Lyria 3 and much more!

Aravind Putrevu — Fri, 20 Feb 2026 13:10:22 GMT

Welcome to the 99th edition of DevShorts, Dev Catch Up.

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Some recent issues from Dev Catch up:

Join 8700+ developers to hear stories from Open source and technology.

Must Read

Google has released Gemini 3.1 Pro. It brings better reasoning, stronger coding and longer context. It is now available in public preview across AI Studio, the Gemini API, Antigravity, and other Google platforms. Check Google’s announcement on Gemini 3.1 Pro for details.
Anthropic has released Claude Sonnet 4.6, the most capable Sonnet model yet. It improves coding, reasoning, agent planning, and computer use. It also supports a 1M token context window in beta. Pricing remains the same as Sonnet 4.5. Check Anthropic’s announcement on Claude Sonnet 4.6.
Alibaba has released Qwen 3.5. This is the first open weight model in the Qwen 3.5 series, named Qwen3.5 397B A17B. It is a native vision language model. It performs well on reasoning, coding, agentic tasks, and multimodal understanding. They also introduced Qwen3.5 Plus, a cloud hosted version. Check the Qwen 3.5 announcement for details.
Elon Musk announced Grok 4.2. It has been used internally for research and is now rolling out to the public. It can learn rapidly from every task it handles. Updates are expected weekly, along with release notes. Check the Grok 4.2 announcement on X for details.

OSS Highlight of the Week

This week we are featuring Entire CLI. It hooks into git and captures AI agent sessions on every push. It saves the prompt and response history along with the commit, so you can see why code changed. It also lets you rewind to a checkpoint when an agent goes wrong. Check the Entire CLI GitHub repo for details.

OpenAI introduced Lockdown Mode in ChatGPT. It is an optional setting that reduces risk from prompt injection by limiting risky connections. They also added Elevated Risk labels to flag higher risk features. Check OpenAI’s announcement for details.
A detailed deep dive breaks down how Codex is built from the ground up. It covers how Codex started, how it works, the most used skills in Codex, and even how Codex helped build itself. Check the substack post to know interesting details about codex.
NotebookLM added prompt based revisions for slides. You can refine a deck with simple prompts. It also supports PPTX export now. Google Slides export is coming next. This should make it easier to move from notes to a shareable deck faster. Check the NotebookLM post for details.
Manus introduced Manus Agents for messaging apps. Telegram is the first supported channel. It is available now for all users across all subscription tiers, and more platforms are coming soon. Check the Manus announcement for details.
Chrome introduced WebMCP in early preview. It gives websites, a standard way to expose structured tools. So AI agents can interact with sites directly in a faster and more reliable way. Check the Chrome Developers post for details.

Notable FYIs

Andrej Karpathy shared a new project called microgpt. It trains and runs a GPT in a single Python file. The file is about 200 lines with no dependencies and includes the core pieces like tokenizer, model, training, and inference. Check Andrej Karpathy’s microgpt post for details.
Excalidraw now has an official MCP server. You can use it in Claude by selecting Excalidraw in Connectors. You can also use it with other AI tools. Check the Excalidraw post for details.
Google has launched Code Wiki. It turns your repository into auto generated documentation and diagrams. It updates docs when code changes and lets you chat with the repo using Gemini. It is now in public preview for open repositories. Check Code Wiki for details.
Google has released Lyria 3. It is their latest generative music model. It can create a 30 second soundtrack with lyrics from your prompt. It is now available in the Gemini API. Check Google’s announcement on Lyria 3 for more details.
Cursor introduced Plugins. It is now available in the Cursor Marketplace. You can install integrations like Figma, Linear and others, and use them through MCP inside the IDE, CLI, and Cloud. Plugins can bundle rules, skills, agents, commands, MCP servers, and hooks into one package. Check the Cursor Plugin Marketplace for details.

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OpenClaw Workflow and Automation - The Developer Guide

Aravind Putrevu — Thu, 12 Feb 2026 13:31:06 GMT

OpenClaw (earlier Moltbot and even earlier Clawbot) got really popular a few weeks ago. Once I kept seeing it everywhere, I wanted to try it on my own day to day workflows.

What I liked first was the variety of channels OpenClaw supports. I can use it from Slack, WhatsApp, and Telegram. I just message it there, and it runs the task for me.

But that power is also where the risk starts. So, it is better to run OpenClaw in an isolated environment where it does not have direct access to your main machine.

I spent time using it on real day to day tasks and picked a few practical workflows that are worth sharing. I will start with the basics, then move into the workflows.

Here is what we will cover.

What is OpenClaw
Installation and Setup for Local, Docker, and Cloud
OpenClaw Workflow and Automation
1. How I fix GitHub issues from slack
2. How I use cron to check pull requests and spot the ones ready to merge
3. How I setup daily AI updates through WhatsApp
Adding Custom Skills to OpenClaw
OpenClaw Agent in Moltbook

1. What is OpenClaw

OpenClaw is an open-source personal AI assistant that runs on your own machine. You can talk to the AI assistant from chat apps such as WhatsApp, Telegram, Slack, Discord, and Teams.

The diagram below shows how it works internally. What impressed me most is how clearly OpenClaw separates the agent from the rest of the system. The gateway, configuration, and lifecycle components are well organized. So, it becomes much easier to understand and debug when something goes wrong.

Gateway

Messages come in from channels like WhatsApp, Slack, or web chat. The gateway receives them and sends them to the right agent. It also keeps the session active and queues follow ups if a run is already happening.

Agent

The agent is the brain. OpenClaw builds the prompt and context, then passes your message to the agent. The agent decides the next step, uses tools if needed, and replies back.

Tools and Skills

Tools do the actual work. They can run shell commands, browse, call APIs, read files, or send messages. Skills are reusable workflows that tell the agent how to use those tools for a task.

Memory

Memory helps the agent remember. It stores project notes and past context so you do not have to repeat the same details every time.

This diagram shows how messages flow through OpenClaw from user input to agent reasoning and action.

2. Installation and Setup

Local Installation

Local setup is straightforward. Run this one-line command.

curl -fsSL https://openclaw.ai/install.sh | bash

You will then go through a short onboarding flow where you choose options based on your setup. It’s a simple process, and you can always configure channels and skills later.

Docker Installation

If you are not comfortable running OpenClaw directly on your system, Docker is a great option. It keeps the environment more isolated and safer.

git clone https://github.com/openclaw/openclaw
cd openclaw
./docker-setup.sh

This time, I used manual onboarding so I could follow each step clearly.

Once installed, you will typically see two key locations.

~/.openclaw - stores config, memory, and keys
~/openclaw/workspace - the agent workspace

If your dashboard is not connecting, then try this.

docker compose run --rm openclaw-cli dashboard --no-open

Other hosting methods

OpenClaw already provides clear guides for hosting on Render, Railway.

OpenClaw deployment on Render
OpenClaw deployment on Railway
You can also run OpenClaw on Cloudflare using moltworker.

3. OpenClaw Workflow and Automation

One part I really wanted to test was how OpenClaw fits into day-to-day workflow, especially for development tasks.

Slack Integration

For my testing, I connected Slack and WhatsApp. Before connecting Slack to OpenClaw, I first created a Slack app. OpenClaw already has clear documentation for Slack app setup.

Here are the exact steps I followed.

Create the Slack app + enable Socket Mode

Go to https://api.slack.com/apps
Click Create New App → From scratch → Give App Name → Select Workspace
Open Socket Mode and turn it ON → In Basic Information → App-Level Tokens, generate a token with scope connections:write
Copy this App Token (xapp-...)
Go to OAuth & Permissions
Add bot scopes (minimum needed):
chat:write, app_mentions:read, channels:history, groups:history, im:history, im:read, im:write, mpim:history, reactions:read, reactions:write, pins:read, pins:write, commands
Go to Install App → Click Install to Workspace
Copy the Bot User OAuth Token (xoxb-...)

Once the Slack App is created, use openclaw configure to connect the slack app.

How I fix GitHub issues directly from slack

Once Slack is connected, I can talk to OpenClaw directly from Slack and use it to handle GitHub issues.

When a new issue comes in, I ask OpenClaw to take a look. It reads the issue, checks my local code, makes the fix, and pushes the change back to GitHub. It also posts a reply on the issue with a short note on what it fixed.

In short: I can go from issue → fix → update, without constantly switching tools.

How I use cron to check pull requests and see which ones are ready to merge

As a GitHub repository maintainer, I like checking which pull requests are merge-ready first thing in the morning. So I set up a daily cron job in OpenClaw.

Every morning, it checks recent PRs and verifies whether the basic merge conditions are met, such as.

no merge conflicts
required CI/status checks passed
required approvals completed

This gives me a quick “merge-ready vs not-ready” snapshot without manual checking. Setting up cron in OpenClaw is simple. You can either.

use the CLI (openclaw cron add), or
ask the agent in chat to create it for you.

Here is how i get my daily PR Report.

How I set up my AI Daily Digest with OpenClaw + WhatsApp

I wanted a daily AI news summary delivered to my personal WhatsApp, using newsletters that already land in my Gmail label called “AI digest.”

What I wanted

Source: Gmail label - AI digest
Output: Concise AI summary to my WhatsApp
Schedule: 10:00 AM IST daily
Query support: “what’s the last week updates?”

At first, I considered Gmail push automation (webhook/Pub/Sub) using the official docs. But that route needed extra infrastructure (like Tailscale). I tried it, but Tailscale was blocked on macOS in my setup, so I dropped that approach and moved to a simpler CLI + cron flow.

Whatsapp Integration

Setup: gog CLI + OpenClaw cron

Here’s the exact setup I used.

Install - gog CLI (Google Workspace CLI) on the host.
Configure Google OAuth client credentials for gog.
Enable Gmail API in Google Cloud Console.
Get/download Google OAuth client JSON.

gog auth credentials set /path/to/client_secret.json

Authenticate Gmail account for CLI access

gog auth add "your-email"  --services gmail

Test auth
```
gog auth list
```

Test search query (last 24h email from AI Digest)

gog gmail messages search "label:\"AI digest\" newer_than:1d" --account "your-email-id" --max 50 --json

Set up cron job in OpenClaw

openclaw cron add \
  --name "Daily AI digest from Gmail label" \
  --session isolated \
  --cron "0 10 * * *" \
  --tz "Asia/Kolkata" \
  --message 'Using gog with account "your-email-id", check Gmail label "AI digest" for emails from the last 24 hours. Extract the most important AI updates/news, deduplicate repeated stories, and send a concise WhatsApp digest with: (1) top updates as bullets, (2) why each matters in one line, and (3) links/sources when available. If no relevant emails are found, say there were no major AI updates in the last 24 hours.' \
  --announce \
  --channel whatsapp \
  --to "your-whatsapp-number" \
  --timeout 180000 \
  --timeout-seconds 120 \

Once this is active, OpenClaw automatically delivers the AI digest to WhatsApp through its messaging route.

Notes/Troubleshooting
If cron fails with "missing refresh token" then do re-auth again.
gog auth add "your-email-id" --services gmail
Always configure both gateway and agent timeouts for cron jobs.

Openclaw commands

These are the commands I used while working with OpenClaw

openclaw configure — Runs interactive setup after installation.
openclaw dashboard --no-open — Opens the OpenClaw UI in browser.
openclaw devices list — Shows paired devices and their status.
openclaw plugins list — Lists installed/available plugins and their state.
openclaw security audit — Runs a standard security audit.
openclaw security audit --deep — Runs deep security audit.

openclaw status — Quick overall health check of gateway, channels etc..
openclaw doctor — Diagnoses common setup/runtime issues and suggests fixes.
openclaw gateway status — Checks whether the gateway service is running.
openclaw gateway restart — Restarts the gateway service (very useful after config/auth changes).
openclaw logs — Shows gateway logs for debugging.
openclaw cron list — Lists all scheduled cron jobs.
openclaw cron runs —-id — Shows execution history for a specific cron job.
openclaw cron run — Manually triggers a cron job for testing.
openclaw cron rm — Deletes a cron job.
openclaw message send ... — Sends a test/proactive message (useful for validating delivery routes).

For more commands: https://docs.openclaw.ai/cli

4. Adding Custom Skills

You can add custom skills to openclaw. Check skills.sh. Select the skill you want to add. Get inside the workspace and install the skill.

5. OpenClaw Agent in Moltbook

If you are interested about giving your agent a more “social” layer, Moltbook is an interesting add-on to explore.

Moltbook - Social network for AI Agent

Read this, before you try OpenClaw

OpenClaw is powerful. That is also why you should be careful.

Safety tips

Do not run this on your primary machine.
Use Docker or a VM or a spare laptop.
Give only the permissions you need.
Treat it as an experiment, not production.
Stick to official sources for downloads and updates.

Conclusion

Now you have seen what OpenClaw is and a few workflows and automations. For daily work, it can save time and reduce tool switching.

I am still exploring more workflows and use cases. If you are experimenting with OpenClaw, start with one simple workflow and build from there.

Hope this post helps you get started.

Happy building!

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[Dev Catch Up # 98] - Claude Opus 4.6, GPT 5.3 Codex, Qwen3-Coder-Next, Codex App, NanoClaw, PaperBanana,Vercel Lab's json-render, No Ads in Claude, git-rebase, Elevenlab's Skills,Xcode is now AI IDE!

Aravind Putrevu — Fri, 06 Feb 2026 13:10:17 GMT

Welcome to the 98th edition of DevShorts, Dev Catch Up.

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Some recent issues from Dev Catch up:

Join 8600+ developers to hear stories from Open source and technology.

Must Read

The coding model race is heating up. We have new releases from different providers this week. Anthropic announced Claude Opus 4.6. We all know Opus 4.5 already performs really well, and they call this new version the strongest model. They also shipped API updates like adaptive thinking, effort levels, and context compaction. Check the Anthropic Opus 4.6 post for details.
OpenAI has introduced GPT-5.3-Codex.. OpenAI says this model is built for agent style coding and long running tasks with tool use. They also mentioned that GPT-5.3-Codex helps build itself. Check the OpenAI GPT 5.3 Codex post for details.
Qwen announced Qwen3-Coder-Next. A new open weight model built for coding agents. It is tuned for long code sessions and agent loops. It has a big context window. If you are trying to run a coding agent on your own machine, this is worth a look. Check the Qwen3-Coder-Next for details.
Kling AI announced Kling 3.0, and it looks like a solid upgrade. It has improved video length and consistency. It also has image generation with 4K image output. It has native audio supporting multiple characters with multi-language. Check Kling AI announcement on Kling 3.0 for details.

OSS Highlight of the Week

This week we are featuring json-render from Vercel Labs. It helps you build AI generated UI without letting the model output raw HTML. The model outputs a JSON layout using only the components you allow. Then json-render reads that JSON and renders the UI in your app. Check the json-render GitHub repo for details.

PaperBanana is a research project for making clean academic figures. It uses a multi-agent pipeline to look at similar examples, picks a layout, keeps the style consistent, then generates the final illustration. Check PaperBanana for more details.
Every week we mention articles about API techniques and security basics. This ByteByteGo post talks about top authentication techniques. Another Substack post discusses 16 API concepts like versioning, rate limiting, and more. If you are working on backend, check these to know the API concepts.
Claude just took a clear stance on ads. They said ads are coming to AI, but not to Claude. It also reads like a pointed response to the recent talk around ad supported ChatGPT plans. Check Claude’s tweet on Ads on AI.
If git rebase still scares you, this post is for you. It explains what rebase does in plain words. It also shows a safe way to use it without breaking your branch history. Check the post on Git Rebase for the Terrified for details.
If you ever used OCR models and wondered how modern document extraction works, this post is a good read. It explains why classic OCR struggles with different templates and why teams move to vision LLMs. Check the post on building a vision LLM to know more details.

Notable FYIs

OpenAI introduced the Codex app. It lets you run multiple coding agents in parallel. It has built in support for worktrees, so agents can work on the same repo without conflicts. It supports Skills too. Check OpenAI’s Codex app announcement for more details.
OpenAI also introduced OpenAI Frontier. It is a platform to build and run AI agents for enterprise workflows, with permissions and controls. Check the OpenAI Frontier announcement for details.
ElevenLabs has released skills. It includes text to speech, speech to text, sound effects, and music generation skills. You can plug these into agents and build relevant applications faster. Check the ElevenLabs skills for details.
Last week we covered about OpenClaw. NanoClaw is a smaller version of the same idea. It runs in containers so you can keep things more isolated. It focuses on WhatsApp workflows, with features like memory, scheduling, and skills. Check the NanoClaw repo for details.
Apple just turned Xcode into an agent native IDE. It lets coding agents like Claude Agent and Codex work inside the project. Xcode now supports MCP, so other MCP compatible agents can plug into Xcode tooling too. Check the Apple Newsroom post for details.

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[Dev Catch Up # 97] - Moltbot (OpenClaw), XAI's Imagine API,OpenAI's Prism, Interactive tools in Claude, Deepseek-OCR-2, RepoBar, Pipenet, Moltbook- Social Network for AI Agents, agent-browser & more!

Aravind Putrevu — Fri, 30 Jan 2026 13:45:37 GMT

Welcome to the 97th edition of DevShorts, Dev Catch Up.

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Some recent issues from Dev Catch up:

Join 8600+ developers to hear stories from Open source and technology.

Must Read

An open source AI assistant named Moltbot, formerly Clawdbot, took the internet by storm this week for its agentic capabilities. It is now renamed to OpenClaw. It runs on your own device. It answers on the channels you already use like WhatsApp, Telegram, Slack, Discord, Google Chat, and Teams etc. It supports many models. Check the Moltbot (OpenClaw) GitHub repo for more info.
Grok has released Imagine API. It is a video generation API that turns text or images into videos with native audio. It debuts at the top of the video model leaderboards. It costs $4.20 per minute. Check xAI’s announcement on Imagine API for more details.
Your favorite work tools are now interactive inside Claude. You can open and use apps like Slack, Figma, Canva, and more right inside the chat. Anthropic says this is powered by MCP Apps, which brings UI support to the MCP client. Check Anthropic’s announcement on Interactive tools in Claude for more details.
DeepSeek released DeepSeek-OCR-2. An open source OCR model that reads documents like humans. It improves layout understanding and handles complex text and tables better than older OCR systems. Check DeepSeek-OCR-2 for more details.

OSS Highlight of the Week

This week we are featuring RepoBar. It shows the status of GitHub repos in the menu bar and in terminal. You can see pull requests, number of issues, commits and more without opening GitHub in the browser. Pin the repos and get a clear dashboard for each repo. Check RepoBar GitHub for more details.

OpenAI introduced Prism, a free AI workspace for scientific writing and collaboration. It is powered by GPT-5.2. Prism gives researchers a cloud based LaTeX environment where they can draft papers, revise text and work with collaborators . Check OpenAI’s Prism for more details.
I read a Substack post featuring Peter Steinberger, the creator of Moltbot. He shares how he ships fast using AI tools and agent workflows. If you want to read the full story, check the post titled - The Creator of Clawd: "I ship code, I dont read".
We all know API is the communication layer between server and client. ByteByteGo published a deep dive on how to scale an API. It explains why scalability matters and the common patterns used to handle spikes. Check the post How to Scale an API for more details.
After Claude Cowork dropped, LangChain quickly released OpenWork. It is a fully open source desktop agent that runs locally. It gives Claude Code style file access and task execution on your own machine. Check the OpenWork GitHub repo for more details.
Google rolled out AI upgrades to Chrome. It is powered by Gemini 3. It adds a new side panel for integrations with Gmail, Calendar, Maps, Flights, Shopping, and more. Chrome also adds auto browse for multi step workflows. Check Google’s announcement on AI Upgrades in Chrome.

Notable FYIs

Claude Code’s new update will be very useful to developers. It can now show updates about your active PR directly in the Claude Code’s footer. Make sure you have the GitHub CLI installed. Check this Twitter post on Claude Code with PR footer for more details.
xAI published the full codebase for the algorithm that powers the "For You" feed on X. It shows how posts are selected and ranked for users using signals and AI models. If you are working one any recommendation system, this may be helpful. Check the xAI algorithm GitHub repo for more details.
I came across Moltbook and found it interesting. Moltbook is a social network for AI agents, where agents post, discuss, and upvote content. There are already threads where agents are discussing with each other. Humans can watch and interact as observers. Check Moltbook for more details.
Developers often expose a local server to the public internet. There are many tools for this already. Pipenet has joined the list. Unlike other tools, it can be installed with a simple npx command. Check Pipenet GitHub Repo for more details.
A tool called qmd caught my attention. A mini CLI search engine for your docs, notes, meeting transcripts, and knowledge bases. It runs locally and lets you search all your markdown and files using natural language. Check the qmd GitHub repo for more details.
If your agent needs browser capabilities, agent-browser is worth a look. It is a headless browser automation CLI for AI Agents. It can open pages, click, fill forms, and take screenshots. It uses fewer tokens compared to Playwright. Check the agent-browser GitHub repo for more details.

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Claude Skills - Automate your workflows

Aravind Putrevu — Fri, 23 Jan 2026 14:31:25 GMT

We all know many GitHub repos now list Claude as a contributor. Even Claude's Cowork was built entirely using Claude Code. Developers have adopted Claude Code. They build custom workflows using hooks, MCP servers, and slash commands to ship faster.

Claude Skills are the newest addition to this workflow. Skills teach domain expertise.

Anthropic released Claude Skills in October 2025. They added document creation & handling capabilities to Claude using Skills. They also open-sourced the production Skills that power Claude's capabilities.

Skills unlock full potential when combined with other tools. You can combine skills with hooks and MCP Servers to automate your workflows. Since Skills launched, developers use Claude as a general-purpose agent for more than just coding.

So, I’m covering about Claude Skills and how I use them in my workflow.

What are Claude Skills
Why Claude Skills
Skills Vs MCP
Automate - Git Commit, PR workflow with Claude Skills
Automate - Invoice Generation with Claude Skills
Top Claude Code skills

1. What are Claude Skills

Skills are folders with a SKILL.md file containing instructions. The folder can also include scripts, templates, and reference docs. Each skill is self-contained in its own folder. Skills teach Claude how to complete specific tasks. They are reusable too. Claude loads the right skill when its needed.

SKILL.md starts with YAML metadata and then the instructions. The metadata includes fields like name and description. Claude uses the description to decide when to apply the Skill.

A skill is not just a markdown file. It can include code samples, reference docs, and templates in the same folder.

In Claude Code, you can keep Skills in two places.

Personal Skills go in ~/.claude/skills/. It can work across projects.
Project Skills go in .claude/skills/. You can commit them and share them with your team.

A big advantage of Skills is they are not loaded fully all the time. Claude reads small metadata first to identify the right skill. Then it loads full instructions only when needed. This keeps your context clean. You can use heavy skills with scripts and docs without bloating tokens.

Skills also work across Claude app, Claude Code. You can also integrate skills to your agent.

2. Why Claude Skills

Here is why Claude Skills are worth adding to your workflow.

3. Skills Vs MCP

Here is a quick side by side view of how Claude Skills and MCP differ.

4. Automate - Git Commit, PR workflow with Claude Skills

Git commits and PR creation happen daily in my work. Running type checks, linting, writing commit messages, and filling PR descriptions add up over time.

I built a skill to automate this workflow. The skill manages everything from pre-commit checks to PR creation. It runs quality checks before every commit, commits the code with proper messages, and automatically raises pull requests with descriptions.

Claude Code handles commits and PRs natively with Git CLI. But it won't follow your exact patterns. I needed standardized commit messages, specific PR templates, and quality checks in my preferred order. So, I built a skill that enforces my workflow.

The Setup

I have three components in my workflow.

CLAUDE Skill - Workflow Orchestrator

It defines the commit-to-PR workflow. It summarizes changes, proposes commit messages, runs pre-commit checks, and orchestrates PR creation with proper descriptions.

Hook for Pre-commit Checks

It intercepts git commit commands. It runs lint, type check, and tests automatically. When checks fail, it blocks the commit and returns errors.

GitHub MCP Server - PR Automation

GitHub MCP Server handles all GitHub operations. It connects to the GitHub API and creates pull requests with descriptions, labels, and reviewers.

How It Works Together

I invoke skill- /commit-guardian
    ↓
Skill summarizes changes, proposes commit message
    ↓
Hook intercepts git commit and runs checks
    ↓
Checks fail? → Hook blocks and returns errors. Skill guides Claude to fix and retry
    ↓
Checks pass? → Commit proceeds
    ↓
Skill invokes GitHub MCP Server
    ↓
MCP creates/updates PR
    ↓
Done - PR URL returned

Setting Up the Hook

The hooks configuration lives in .claude/settings.json. You can create this using /hooks. You can also define hooks inside Claude Skill. This hook will run during Claude Skill’s lifecycle.

{
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Bash",
        "hooks": [
          {
            "type": "command",
            "command": "python .claude/hooks/commit_guard.py"
          }
        ]
      }
    ]
  }
}

Our hook executes the script - .claude/hooks/commit_guard.py. Here’s the script structure.

#.claude/hooks/commit_guard.py
# Pre-commit quality gate - runs lint, typecheck, tests

import json
import subprocess
import sys

def detect_package_manager():
    """Returns npm, yarn, or pnpm based on lock files."""
    # ...

def run_checks():
    """Runs lint, typecheck, and tests. Returns results."""
    # ...

def main():
    raw = sys.stdin.read().strip()
    data = json.loads(raw)
    
    # Only intercept git commit commands
    if "git commit" not in data.get("tool_input", {}).get("command", ""):
        return 0
    
    results = run_checks()
    failed = [r for r in results if not r[1]]
    
    if failed:
        sys.stderr.write("Commit blocked - checks failed\n")
        return 2  # Block commit
    
    return 0  # Allow commit

if __name__ == "__main__":
    raise SystemExit(main())

Creating the Skill

I created the skill based on my requirements. My commit messages follow a standard format with conventional commits using type and scope. PRs need specific templates with changes, motivation, and testing sections. The skill defines these rules. Claude follows them exactly every time.

The skill lives at .claude/skills/commit_guardian/SKILL.md.

---
name: commit_guardian
description: Manages commit workflow with quality checks and automated PR creation. Use when committing code or creating pull requests.
allowed-tools:
  - Read
  - Edit
  - Write
  - Bash
---

# Commit Guardian Workflow

## Process

1. **Pre-Commit Summary**
   - List staged files
   - Summarize changes (2-3 lines)

2. **Commit Message**
   - Format: `type(scope): description`
   - Types: feat, fix, docs, style, refactor, test, chore
   - Under 72 characters

3. **Quality Checks**
   - Hook runs: lint, typecheck, tests
   - If fail: explain, fix, rerun
   - Repeat until pass

4. **PR Creation**
   - Get branch and target
   - Use GitHub MCP to create PR
   - Add title, description, labels, reviewers

## PR Template
```
## Changes
[What changed]

## Motivation
[Why needed]

## Testing
- [ ] Tests added/updated
- [ ] Manually tested
- [ ] All checks passing
```

Setting Up GitHub MCP

Install the GitHub MCP server to Claude Code. It handles all GitHub operations.

claude mcp add-json github '{"type":"http","url":"https://api.githubcopilot.com/mcp","headers":{"Authorization":"Bearer YOUR_GITHUB_PAT"}}'

Testing the workflow- Commit to PR Skill

You can manually invoke skills using their name. I invoked /commit-guardian to test the workflow. Claude can also invoke skills automatically based on context.

As this is my daily routine, I customized this flow with skills. So that it can complete the flow just like me.

Get the Code

Above mentioned skill files, scripts are available at this GitHub Repo.

5. Automate Invoice Generation with Claude Skills

I work on small projects often. I track my hours, calculate totals, and fill the invoice template. Simple work, but it adds up.

So, I automated it using Claude Skills. The goal is straightforward. Track time while I work, calculate billable hours, and generate a ready-to-send invoice PDF.

The Flow:

Slash commands

I created slash commands to log start and end times. The commands live in .claude/commands/.

/start-work logs the start time. /stop-work logs the end time.

If you want, you can add /pause for breaks or customize commands for your workflow.

CLAUDE Skill

The Claude skill reads .work_log.json and totals hours for the selected period. It fills the invoice PDF template using the bundled script and generates the final invoice.

How it works

Get the Code

The invoice generation skill, slash commands, and scripts are available in the GitHub repo.

6. Top Claude Code skills

Superpowers – Skills that add structured workflow commands like brainstorm and execute plan to organize development tasks.
dev-browser - Claude skills to give your agent the ability to use web browser.
mcp-builder - Skills to create MCP servers
frontend-design - Skills to create production grade frontend design.
pdf-skills - Skills to do pdf operations.
Notion Skills - Skills that teach Claude how to work in Notion.

Want to build your own skills? Check out this article on creating custom skills.

Using Skill Creator

Claude Code has a built-in skill creator. Enable it in settings and describe what you need. Claude generates the skill.md and all the files for you.

Conclusion

We covered Claude Skills and two workflows I use daily for commit automation and invoice generation.

Skills turn repetitive work into reusable instructions. Combine them with hooks and MCP to build reliable automation. See how developers use Claude Skills in production to capture ML experiment results and prevent duplicate work.

If you have repeatable routines in your day-to-day work, you can likely automate them with Skills. Build one skill for your workflow and see how much time it saves.

Happy Learning!

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[Dev Catch Up # 96] - Claude's Cowork, ChatGPT Translate, Google's Universal Commerce Protocol,Gemini's Personal Intelligence, Claude HUD, Top Developer Tools 2026, Anthropic's sandbox-runtime & more!

Aravind Putrevu — Fri, 16 Jan 2026 13:31:37 GMT

Welcome to the 96th edition of DevShorts, Dev Catch Up.

Thanks for reading Dev Shorts! Subscribe for free to receive new posts and support my work.

Some recent issues from Dev Catch up:

Join 8600+ developers to hear stories from Open source and technology.

Must Read

Anthropic has launched Claude Cowork. When developers started using Claude Code, they did more than code. They used it for document analysis, report writing, and more. Anthropic saw this and shipped Cowork. You pick a folder and describe the task. Cowork can handle the rest with more autonomy than normal chat. Check Claude’s Cowork for more details.
OpenAI launched ChatGPT Translate. It is a standalone translation tool that feels close to Google Translate. It still misses features like document upload, website translation, and more. Language support also seems smaller for now. Try ChatGPT Translate to see its features.
Google launched Universal Commerce Protocol. It is a new open standard for AI shopping, from search to checkout and support. Google plans to bring UCP powered checkout into the Gemini app soon, so users can buy inside the chat. Check Google’s UCP to know more details.
We all use Git. But many of us do not know how it works inside. This deep dive explains how Git tracks changes without using huge storage. It also shows what happens during add, commit, and branch. Check this Substack post on How Git Works Internally to know more details.

OSS Highlight of the Week

This week we are featuring Claude HUD. It is a Claude Code plugin that shows what is happening while Claude runs. It displays context usage, active tools, running agents, and todo progress right below your input. Check the Claude HUD GitHub repo to know more.

ByteByteGo shared a simple map of top developer tools for 2026. It lists common developer tools, like editors, Git, testing, CI CD, containers, and project boards. It also adds api testing and ai coding tools. Handy if you are updating your stack or onboarding a new team.
Anthropic has introduced Labs. It is a team that ships experimental features and then scales what works. Anthropic says this approach helped Claude Code move from a research preview to a billion dollar product in six months. Check Anthropic’s Labs post for more details.
As your app grows, the database is often the first thing to slow down. I came across a Substack post that explains why databases are hard to scale. It also explains sharding, and how splitting data across servers can help. Check the substack post on Database Sharding to know more details.
Cursor shared what happens when you run hundreds of coding agents for weeks. They used planners, workers, and a judge loop to keep the work on track. One run built a working web browser from scratch, and other runs shipped huge codebase tasks too. Check Cursor’s blog on Scaling long-running autonomous coding for more details.
Apple will use Google’s Gemini models to power a new Siri upgrade coming this year. It is a multi year deal, and it is Apple’s biggest AI bet yet after the Siri upgrade delays. Check the joint statement from Google and Apple for more details.

Notable FYIs

Anthropic open sourced Sandbox Runtime. It is a research preview built for Claude Code to make agent runs safer. It can limit a process from accessing your files and network, without using a container. Check Anthropic’s Sandbox Runtime for more details.
Google launched Personal Intelligence for Gemini. It connects Gmail, Photos and Search. You can now ask about your inbox, photos, or search history and get a personal answer. Google says your inbox and photo library data will not be used for training. It rolls out first in the US for Pro and Ultra users. Check Gemini’s Personal Intelligence for more details.
If you spend too much time finding the right Claude Code skills, try SkillIssue. It helps you find and pick the right skills from different marketplaces. It also explains why each one fits and gives install steps you can copy and run. Check SkillIssue for Claude for more details.
Slack rebuilt Slackbot as an AI agent for work. It uses the messages and files you already can access, and it follows the same permissions. It can help you catch up fast, pull decisions, and draft content in your voice. Check Slackbot for more detail.
Google updated MedGemma with MedGemma 1.5. It adds better support for medical imaging. They also released MedASR, an open medical speech to text model. Check MedGemma 1.5 and MedASR if you are working on healthcare use-cases.

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[Dev Catch Up # 95] - ChatGPT Health, AI Inbox in Gmail, How Google's TPU works, dev-browser, Alexa in web, TAWS, Snitch, Nvidia's Rubin GPU, Antigravity Claude Proxy, Falcon H1R 7B and & much more!

Aravind Putrevu — Fri, 09 Jan 2026 12:55:24 GMT

Welcome to the 95th edition of DevShorts, Dev Catch Up.

Thanks for reading Dev Shorts! Subscribe for free to receive new posts and support my work.

Some recent issues from Dev Catch up:

Join 8600+ developers to hear stories from Open source and technology.

Must Read

OpenAI has announced ChatGPT Health. It adds a new health tab inside ChatGPT. You can now link your medical records and health apps. ChatGPT answers health related questions using your own data. It explains diet and workout ideas from your health history. It keeps your health data private. Read the ChatGPT Health announcement for more details.
Google is bringing Gemini into Gmail. Gmail can now summarize long email threads. It can answer questions about your inbox. It also helps you write replies faster. It highlights important emails and tasks. Read the Google’s announcement on Gmail in Gemini Era to learn more.
Many of us are using Claude Code. Its creator Boris shared how he uses it every day. He runs many Claude sessions and hands them off between terminal and web. He uses subagents to simplify code and verify changes. He also tags @claude on PRs to improve CLAUDE.md. Check Boris’s Claude Code setup for more tips.
We all know Google uses its own hardware to run models. This Substack post explains how Google’s TPU works. It shows why Google built TPUs for neural networks. They move less data and do more math. Read the full post to understand how TPUs power large AI models.

OSS Highlight of the Week

This week we are featuring TAWS. It is a terminal UI for AWS. With taws, you can interact with AWS resources directly from the terminal. It supports multiple regions, profiles and over 90 AWS resources with detailed views. Check the TAWS GitHub repo to know more.

Amazon has launched Alexa.com. It is a browser version of the new Alexa+ assistant. Alexa now works on the web too. You can type to Alexa and get your queries answered. This puts Alexa in the same space as ChatGPT and Gemini. Read the Alexa.com announcement to learn more.
The Substack post looks at what happens to software engineering when AI writes most of the code. It shows how models like Opus 4.5 and GPT 5.2 now ship production code. As a result, many engineers no longer open an IDE. Read the full post to know more details.
NVIDIA unveiled the Rubin platform for AI supercomputers. It combines six new chips into one tightly integrated system. Rubin trains large models faster and serves more tokens at the same power. This helps bring down training time and inference cost. Read the Rubin platform announcement to learn more.
If you work with multiple branches often, this tool is worth a look. Worktrunk is a CLI for managing Git worktrees. It helps you work on parallel branches. It is useful when running multiple tasks or agents. Check the Worktrunk GitHub repo to learn more.
I came across a tool named Snitch. It is a terminal tool for inspecting network connections. It shows active sockets in a clean table. You can filter and explore connections easily while debugging. Check the Snitch GitHub repo to learn more.
Andrej Karpathy shared a small nanochat experiment. He trained many model sizes in a few hours on 8 H100 GPUs at very low cost. Smaller models trained in minutes. He also found a new scaling rule. It trains bigger models for less time using a faster optimizer called Muon. Read the full nanochat discussion to learn more.

Notable FYIs

Technology Innovation Institute released Falcon H1R 7B. It is a new 7B parameter reasoning model. It performs better than larger models on coding and reasoning tasks. It also supports long context to keep high accuracy. Read the Falcon H1R 7B announcement to learn more.
I came across antigravity-claude-proxy. It is a proxy server to use Antigravity models with Claude Code. It lets you access Claude Code using a Google AI Pro subscription through supported models. Check the antigravity-claude-proxy GitHub repo to learn more.
If you are looking for a mobile client for Claude Code and Codex, check out Happy. It lets you access and control coding sessions from your phone or browser. Useful when you want to manage your coding agents without a laptop. Check out Happy, the mobile client for coding agents.
Browser automation is often used for testing and web tasks. If you want your agent to handle this, check dev-browser. It lets agents open web pages, click links, fill forms, and read content. It is faster and cheaper than Playwright and browser extensions. Check the dev browser GitHub repo to learn more.
While checking Claude Code skills, I came across Superpowers. It adds structure to how coding agents work. It guides agents through brainstorming, planning, and agent driven development. This helps agents build better software. It feels like the agent gets superpowers. Check the Superpowers GitHub repo to learn more.

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[Dev Catch Up # 94] - GLM 4.7, Claude in Chrome, Manus joins Meta, Nvidia Groq Deal, IQuest-Coder-V1, Gitmal, Qwen3-TTS, Deepseek's mHC, Alibaba's GUI Agents, Meta's Self Play SWE-RL and much more!

Aravind Putrevu — Fri, 02 Jan 2026 13:40:22 GMT

Welcome to the 94th edition of DevShorts, Dev Catch Up.

Happy New Year 2026!!!

Thanks for reading Dev Shorts! Subscribe for free to receive new posts and support my work.

Some recent issues from Dev Catch up:

Join 8500+ developers to hear stories from Open source and technology.

Must Read

Claude finally comes to the browser. It can navigate pages, click buttons, fill forms, pull analytics, organize Drive files, clean your inbox, etc. It is in beta for paid users. Anthropic highlights safety risks, so start with trusted sites only. Read about Claude in Chrome to learn more details.
Z.ai just released GLM 4.7. It is an open-source coding model. It crosses 70% on SWE benchmarks. It focuses on coding, reasoning, and tool usage. It is priced much lower than Claude and targets developer agents. Read about GLM 4.7 Coding Model to learn more.
A new generation of code models called IQuest Coder and LoopCoder has been released. They are built for software engineering and competitive programming. The Loop architecture improves throughput and reduces memory usage. Check IQuest Coder to know more details.
Alibaba’s Qwen released two new Flash TTS models. They focus on voice design and voice cloning. One model creates a reusable voice from text descriptions. The other clones a voice from audio and supports around 10 languages. Read about Qwen3 TTS to learn more details.

OSS Highlight of the Week

This week’s OSS highlight is Gitmal. It is a static page generator for Git repos. It turns your repo into browsable HTML pages with files, commits, syntax highlighting, and markdown rendering. You can run it locally, with Docker, or self-host it for sharing code without GitHub. Check Gitmal GitHub Repo to explore and try it out.

Manus is joining Meta. Manus builds a general-purpose autonomous AI agent. It can handle tasks like research, coding, and data analysis. The service will continue to run and also integrate into Meta AI. Read about the Manus Meta partnership to know more details.
NVIDIA is spending about $20B to acquire Groq’s ultra-fast inference technology and its engineering talents, including the founder, Jonathan Ross. This is NVIDIA’s biggest move so far. It is a licensing deal rather than a full acquisition. Check NVIDIA Groq deal to know more details.
If you are using plugins with Claude Code, check this GitHub repo. It contains official plugins for Claude Code. One of them is the Ralph Wiggum plugin for running iterative agent loops. Check the Claude Code plugins repo to learn more details.
Most developers have started using AI for coding. This Hacker News thread shows a discussion on how to get better at AI programming. A Claude Code team member shares tips like using CLAUDE.md, Plan mode, and letting the model check its own work. The discussion also highlights context loss and why starting fresh chats often works better. Check the discussion to know more.

Notable FYIs

DeepSeek just published new research on model architecture. The paper introduces mHC, a method that improves training stability and scalability with little extra compute cost. Tests on 3B, 9B, and 27B models show better benchmark results. Read the paper to understand where DeepSeek may be heading next.
Google is expanding NotebookLM in two ways. It adds Lecture Mode in NotebookLM. It can convert multiple sources into an AI narrated lecture for passive learning. You can also upload NotebookLM notebooks directly into the Gemini app. Read both updates to know more details.
If you are working with coding agents, check Conductor. It lets you run multiple coding agents at the same time. You can spin up parallel Claude Code and Codex agents in isolated workspaces with a clean UI. It handles git worktrees, reviews, and merges for you. Check Conductor to learn more.
If you are working on image generation, check FLUX.2 dev Turbo. It is a LoRA adapter for the FLUX.2 dev base model. It generates high quality images in just 8 steps. It supports text to image and image editing. Check FLUX.2 dev turbo for more details.
Meta rolled out Self play SWE RL. In this setup, the model writes buggy code and learns by fixing its own mistakes. The work shows agent speed is a system problem, not a model problem. It introduces Agent Infer to reduce token waste and speed up tasks. Read this to learn how Meta improves AI agents.
Tencent released HY Motion 1.0, a text to 3D human motion generation model. It turns simple text prompts into skeleton-based 3D animations. The model scales with better motion quality and instruction following. Check HY Motion 1.0 to learn more details.
Alibaba introduced MAI UI, a foundation GUI agent for mobile assistance. It turns text instructions into real actions across apps like office tools, shopping, travel, and daily tasks. MAI UI shows strong results in GUI grounding and mobile navigation. Read about MAI UI to learn how mobile GUI agents are evolving.

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Finetuning DeepSeek-V3 With Tinker API

Aravind Putrevu — Tue, 23 Dec 2025 10:31:06 GMT

We all know Thinking Machines Lab by now. When Mira Murati, OpenAI’s former CTO, started this company, many people were curious about what they would build first.

They started with Tinker.

Tinker is an API for fine tuning large language models. The idea behind it is simple. You control the training logic. This includes your data, loss functions, and training loop. Tinker takes care of the hard parts like distributed GPU training, scheduling, and system reliability.

In simple terms, Tinker helps you fine tune models without worrying about infrastructure, while still giving you full control.

In this blog, I walk through:

What is Tinker
Why Tinker
Finetuning DeepSeek V3 with Tinker
Inference and Deployment Options

1. What is Tinker

Tinker is an API for fine tuning large language models.

Fine tuning means taking an existing model and training it on your own data. This is how a model learns a new style or task.

With Tinker, you still write the training logic. You decide how data is loaded, how loss is calculated, and how the training loop runs.

But you do not have to manage the infrastructure. You call the Tinker API from your local machine, and the training runs on Tinker’s GPUs.

The Tinker API has four functions:

2. Why Tinker

Fine-tuning LLMs typically falls into two extremes.

Manual fine-tuning

It gives you complete control. Your GPUs. Your CUDA setup. Your choice of libraries and optimizers. Every knob is yours to turn.

But so is every failure. Every compatibility issue. Every hour spent on infrastructure instead of actual training.

Platform-based fine-tuning:

It offers simplicity. just upload a dataset, click train, download weights. No infrastructure to manage, no code to write.

But you’re locked out of the training process entirely.

Tinker: The Middle Ground

Tinker fits in the right sweet spot between Manual Finetuning and Platform based Finetuning. It gives you control where it matters.

You define the datasets, loss functions, and training loops. Tinker handles the infrastructure headache like GPU setup, Distributed training, Memory management and Hardware failures.

Tinker gives you full control over fine-tuning without the infrastructure tax.

Both extremes come with some disadvantages, which makes Tinker Existence Valuable.

3. Finetuning DeepSeek V3 with Tinker API

Now let us finetune DeepSeek Model with Tinker API.

I send out the Dev Catch Up newsletter every week. Each item follows a clear style. It is short, simple, and focused on what actually matters. I read AI updates and releases, then rewrite them in my own voice.

I wanted to see if DeepSeek could learn this style.

So, I fine-tuned the model on my newsletter data. After training, it started picking up the Dev Shorts tone. Simple and Short sentences. No unnecessary hype.

Before fine tuning, the base model behaves like most LLMs. It uses bullet points, emojis, and phrases like “game changer” or “massive upgrade”. That is not how I write.

Let us now fine tune it step by step and see how the output changes.

Step 1: Install Tinker

!pip install tinker
!pip install torch
!pip install tinker_cookbook

Step 2: Set Your API Key

import os
os.environ["TINKER_API_KEY"] = "your-api-key-here"

Step 3: Import necessary packages

Tinker is the core training API. It runs the training on managed infrastructure.

Tinker Cookbook is an optional helper library. I am using it here for tokenization, chat formatting, and dataset conversion.
If you want, you can write tokenization and the rest from scratch without using Cookbook and work directly with the Tinker API.

import json
import time
import tinker
import torch
from tinker import types
from tinker_cookbook import renderers
from tinker_cookbook.supervised.data import conversation_to_datum
from tinker_cookbook.renderers import TrainOnWhat
from tinker_cookbook.tokenizer_utils import get_tokenizer
from tinker_cookbook import model_info
from tinker_cookbook.supervised.common import compute_mean_nll

Step 4: Connect to Tinker and see available models:

service_client = tinker.ServiceClient()
capabilities = service_client.get_server_capabilities()

print(”Available models:”)
for model in capabilities.supported_models:
    print(f”  {model.model_name}”)

Step 5: Select the base model to finetune

MODEL_NAME = “deepseek-ai/DeepSeek-V3.1”

This is the base model we are going to fine-tune. The model's name must match one of the available models returned by the Tinker API.

Step 6: Configure LORA Finetuning Parameters

BATCH_SIZE = 8
LEARNING_RATE = 2e-5
LORA_RANK = 32
MAX_LENGTH = 4096
NUM_EPOCHS = 10

LEARNING_RATE = 2e-5 is 0.00002. - Start conservative for large models.
LORA_RANK = 32 - Controls the adapter size. Higher means more capacity but slower training.
NUM_EPOCHS = 10 - means model go through the dataset 10 times.

Step 7: Load Tokenizer and Renderer

tokenizer = get_tokenizer(MODEL_NAME)
renderer_name = model_info.get_recommended_renderer_name(MODEL_NAME)
renderer = renderers.get_renderer(renderer_name, tokenizer)

The tokenizer converts text into tokens. The renderer defines how chat messages are formatted before they are sent to the model.

Step 8: Prepare the Dataset for Training

My dataset is a JSONL file.

The user message contains raw content scraped from release pages. The assistant message contains the Dev Shorts newsletter style.

I prepared around 100 rows in total. Each row follows this format. For this walkthrough, I am using a small sample of 16 rows.

{
  "messages": [
    {
      "role": "user",
      "content": "Rewrite in Dev Shorts newsletter style. 
We are introducing GPT‑5.2, the most capable model series yet for professional knowledge work.

Already, the average ChatGPT Enterprise user says⁠ AI saves them 40–60 minutes a day, and heavy users say it saves them more than 10 hours a week. We designed GPT‑5.2 to unlock even more economic value for people; it’s better at creating spreadsheets, building presentations, writing code, perceiving images, understanding long contexts, using tools, and handling complex, multi-step projects.

GPT‑5.2 sets a new state of the art across many benchmarks, including GDPval, where it outperforms industry professionals at well-specified knowledge work tasks spanning 44 occupations."
    },
    {
      "role": "assistant",
      "content": "OpenAI has launched GPT 5.2. It is better at creating spreadsheets, building presentations, writing code, understanding images. It can work with long context. It also uses tools more effectively and handles multi-step work with more accuracy. Read OpenAI’s GPT 5.2 post for more details."
    }
  ]
}

Step 9: Load the Dataset

DATASET_PATH = "dataset.jsonl"

training_data_raw = []
with open(DATASET_PATH, "r") as f:
    for line in f:
        training_data_raw.append(json.loads(line.strip()))

print(f"Loaded {len(training_data_raw)} examples")

Step 10: Convert the data to training format

conversation_to_datum - This function converts each conversation into a format Tinker can use for training.

TrainOnWhat.ALL_ASSISTANT_MESSAGES - It tells Tinker to only learn from assistant responses, not user messages.

training_data = [
    conversation_to_datum(
        ex["messages"],
        renderer,
        MAX_LENGTH,
        TrainOnWhat.ALL_ASSISTANT_MESSAGES,
    )
    for ex in training_data_raw
]

Step 11: Create Training Client

training_client = service_client.create_lora_training_client(
    base_model=MODEL_NAME,
    rank=LORA_RANK,
)

print("Training client created")

Step 12: Train the Model

Tinker has 4 core functions. The training loop uses two of them.

forward_backward() - compute gradients
optim_step() - update weights

n_batches = len(training_data) // BATCH_SIZE

for epoch in range(NUM_EPOCHS):
    print(f"\nEpoch {epoch + 1}/{NUM_EPOCHS}")
    
    for batch_idx in range(n_batches):
        t_start = time.time()
        
        # Learning rate decay
        step = epoch * n_batches + batch_idx
        total_steps = NUM_EPOCHS * n_batches
        lr_mult = max(0.0, 1.0 - step / total_steps)
        current_lr = LEARNING_RATE * lr_mult
        
        adam_params = types.AdamParams(
            learning_rate=current_lr,
            beta1=0.9,
            beta2=0.95,
            eps=1e-8,
        )
        
        # Get batch
        batch_start = batch_idx * BATCH_SIZE
        batch_end = batch_start + BATCH_SIZE
        batch = training_data[batch_start:batch_end]
        
        # Training step
        fwd_bwd_future = training_client.forward_backward(batch, loss_fn="cross_entropy")
        optim_step_future = training_client.optim_step(adam_params)
        
        fwd_bwd_result = fwd_bwd_future.result()
        optim_step_future.result()
        
        # Compute loss
        logprobs = [out["logprobs"] for out in fwd_bwd_result.loss_fn_outputs]
        weights_list = [d.loss_fn_inputs["weights"] for d in batch]
        mean_nll = compute_mean_nll(logprobs, weights_list)
        
        elapsed = time.time() - t_start
        print(f"  Batch {batch_idx + 1}/{n_batches} | NLL: {mean_nll:.4f} | LR: {current_lr:.2e} | Time: {elapsed:.2f}s")

print("\nTraining complete")

The loss (NLL) should decrease as training progresses.

Note: The dataset size and epochs shown here are for demonstration. I actually finetuned with more rows and higher epochs to get the model to pick up the style properly.

Step 13: Save Checkpoint

This uses Tinker's third function.

save_weights_for_sampler() - It stores the checkpoint on Tinker's servers.

final_path = training_client.save_weights_for_sampler(name="final").result().path
print(f"Final checkpoint: {final_path}")

Step 14: Test the Finetuned Model

Finally, test the finetuned model using Tinker’s fourth function- sample().

test_client = service_client.create_sampling_client(model_path=final_path)

test_input = (
    "The Gemma is a family of generative AI models designed for tasks like question answering, summarization etc. It is a family of open models from Google. Gemma recently crossed 100 million downloads and has a growing community with tens of thousands of model variants created by developers. Google has now introduced Gemma 3, a new set of lightweight open models built on the same research used for Gemini 2.0. These models are designed to run efficiently on devices like phones, laptops, and workstations. Gemma 3 is available in multiple sizes including 1B, 4B, 12B, and 27B. Gemma 3 supports text and image reasoning, a 128k token context window, function calling, and structured outputs. It also offers support for more than 140 languages and includes official quantized versions to improve performance while reducing compute requirements."
)

STYLE_PREFIX = (
    "Rewrite in Dev Shorts newsletter style."
)

convo = [
    {
        "role": "user",
        "content": STYLE_PREFIX + "\n\n" + test_input
    }
]

model_input = renderer.build_generation_prompt(convo)

result = test_client.sample(
    prompt=model_input,
    num_samples=1,
    sampling_params=types.SamplingParams(
        max_tokens=200,
        stop=renderer.get_stop_sequences(),
        temperature=0.2,
    ),
).result()

parsed_message, _ = renderer.parse_response(result.sequences[0].tokens)
output = parsed_message["content"]

print("INPUT:")
print(test_input)
print("\nOUTPUT:")
print(output)

The finetuned model writes in the style we finetuned.

Output:

Google released Gemma 3. It is a set of open models for tasks like question answering and summarization. It has a 128K token context window and supports text and image reasoning. It is designed to run on phones, laptops, and workstations. It supports over 140 languages. Check Google’s Gemma 3 for more details.

Step 15: Download the finetuned model weights

import urllib.request

sc = tinker.ServiceClient()
rc = sc.create_rest_client()
future = rc.get_checkpoint_archive_url_from_tinker_path("tinker://8c8-------2d0bd:train:0/sampler_weights/final")
checkpoint_archive_url_response = future.result()
urllib.request.urlretrieve(checkpoint_archive_url_response.url, "deepseek_finetuned.tar")

We tested the fine-tuned model using Tinker. Now let us see how to use the fine-tuned weights outside Tinker.

4. Inference and deployment options

As we can download finetuned weights - LoRA adapters, we can also use it outside the Tinker platform.

1. Load the LoRA adapter using Hugging Face PEFT

You can load the LoRA adapter on top of the base model using HuggingFace PEFT. and use for inference.

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V3.1")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V3.1")
model = PeftModel.from_pretrained(base_model, "./deepseek_finetuned")

prompt = "Rewrite in Dev Shorts newsletter style.\n\nOpenAI released GPT-5..."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

2. Run the model with vLLM for fast inference

You can run the base model with the LoRA adapter using vLLM for fast inference.

from vllm import LLM
from vllm.lora.request import LoRARequest

llm = LLM(model="deepseek-ai/DeepSeek-V3.1", enable_lora=True)

output = llm.generate(
    "Rewrite in Dev Shorts newsletter style.\n\nOpenAI released GPT-5...",
    lora_request=LoRARequest("devshorts", 1, "./deepseek_finetuned")
)
print(output[0].outputs[0].text)

Note on hardware requirements

Running large models using Hugging Face or vLLM requires significant GPU memory. Make sure your hardware can handle the base model size.

If you do not want to manage infrastructure, you can also host the model on third party platforms that support LoRA adapters.

Conclusion

Tinker does one thing well. It lets you focus on training behavior instead of infrastructure.

If your goal is to fine tune models, Tinker is worth trying. You stay close to the training loop. You can see how the model changes at each step. And you can still take the weights and run them anywhere you want.

I hope this blog gave you a clear idea of what Tinker is and how it works.

See you in the next Bytes blog,

Until then, happy learning and happy holidays!!!

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[Dev Catch Up # 93] - GPT-5.2-Codex, ChatGPT Images, Gemini 3 Flash, Grok Voice Agent API, Google's A2UI, Claude-mem - Memory for Claude Code, Mistral OCR 3, Runme - Runnable Markdown and much more!

Aravind Putrevu — Fri, 19 Dec 2025 13:03:07 GMT

Welcome to the 93rd edition of DevShorts, Dev Catch Up!

Thanks for reading Dev Shorts! Subscribe for free to receive new posts and support my work.

Some recent issues from Dev Catch up:

Join 8500+ developers to hear stories from Open source and technology.

Must Read

OpenAI has launched ChatGPT Images. It generates images faster and performs precise edits. It is now available via API as GPT Image 1.5. Alongside this, OpenAI also released GPT 5.2 Codex an agentic coding model built for long running tasks. Check both announcements for more details.
Google has released Gemini 3 Flash. It is the latest model in Gemini series built for speed with low cost. It is mainly for coding, agents, and everyday tasks. Gemini 3 Flash is now available across the Gemini app, API, and Vertex AI. Check Google’s Gemini 3 Flash announcement for more details.
xAI has launched the Grok Voice Agent API. It helps developers build voice agents with multilingual support, real time search, and tool calling. It uses the same tech that powers voices in Tesla Vehicles. It costs $0.05 per minute. Worth checking out if you are building voice apps.
Mistral has released Mistral OCR 3 for document parsing. It works better on forms, tables, handwriting, and low-quality scans. It outputs clean text or structured data. It costs about $1 per thousand pages with batch usage. Check Mistral OCR 3 for more details.

OSS Highlight of the Week

This week we are featuring Claude Mem. It gives Claude Code long term memory across sessions and restores useful context automatically. You can search past memory from Claude Desktop. It also includes privacy controls to exclude sensitive data and lets you control what context gets injected. If you are using Claude Code, check the Claude Mem GitHub Repo.

I came across Runme DevOps Notebook. It lets you run shell commands and code blocks directly from Markdown files like READMEs. It supports many runtimes including Shell, Python, Ruby, JavaScript, TypeScript, PHP, and more. Check the Runme GitHub Repo for more details.
This ByteByteGo visual shows different database types for different needs. It includes vector search, graph databases, time series, in memory, and blob storage. It helps you understand which database fits which use case. Check the page to see the databases you should know by 2025.
I came across the article that explains agentic workflow patterns. It shows why prompts alone are not enough. It also explains how structure, iteration, and coordination help agents work more reliably. Check the post on Top AI Agentic Workflow Patterns to know more.
All AI tools work based on context. Context engineering has become important as agents handle longer tasks. This Substack post explains the anatomy of context, context retrieval strategies, and ways to manage long running workflows. Read Context Engineering 101 to learn more.

Notable FYIs

Black Forest Labs has released FLUX.2 max. It is their latest model for image generation. It focuses on consistent characters, precise edits, and clean visuals. Useful if you need reliable image outputs for production.
Google Labs has launched CC Agent. It is an AI agent that connects Gmail, Calendar, and Drive to give you a daily briefing. You can also email CC to get help during the day. Join the waitlist if you want a simple AI assistant for daily planning.
Alibaba has released Wan 2.6. It is a video generation model. It supports text to video, role consistency, audio video sync, and 1080p output. It can generate video up to 15s long. Worth checking if you are exploring video generation models.
Google has open sourced A2UI. It’s a protocol that lets AI agents send interactive UI elements like forms and cards. Instead of sending HTML or code, agents send simple JSON descriptions that apps render using their own native components. Check Google’s A2UI protocol for more details.
Meta has released SAM Audio. It is an AI model that can isolate sounds from complex audio using text, visual, or time-based prompts. You can extract things like vocals, instruments, or background noise with simple inputs. Useful for audio editing and podcasts related workflows.
A few months back, OpenAI released the Apps SDK to build apps for ChatGPT. Now developers can submit ChatGPT apps for review. It will be published in the ChatGPT app directory. If you want your app to reach ChatGPT users, you can submit it now.

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[Dev Catch Up # 92] - OpenAI's GPT-5.2, Mistral's Devstral 2, Slack with Claude Code, Selective Gradient Masking, Walrus - Kafka Alternative, Orchids vibe coding IDE, Composio's Tool Router and more!

Aravind Putrevu — Fri, 12 Dec 2025 13:31:42 GMT

Welcome to the 92nd edition of DevShorts, Dev Catch Up!

Thanks for reading Dev Shorts! Subscribe for free to receive new posts and support my work.

Some recent issues from Dev Catch up:

Join 8500+ developers to hear stories from Open source and technology.

Must Read

OpenAI has launched GPT 5.2. It is better at creating spreadsheets, building presentations, writing code, understanding images, and working with long context. It also uses tools more effectively and handles complex multi-step work with more accuracy. Read OpenAI’s GPT 5.2 post for more details.
Mistral has released its open-source agentic coding models and a new CLI agent. The model's name is Devstral 2. It comes in two sizes, 123B and 24B. Mistral also introduced a terminal coding agent. Check Mistral’s announcement on Devstral 2 and Vibe CLI for more details.
Claude Code can now get tasks directly from Slack. When you discuss bugs or feature requests in slack, you can tag @Claude to start a Claude Code session with the right context pulled from the chat. Read Claude Code and Slack post for more details.
Anthropic has introduced a new method called Selective Gradient Masking. It stores risky knowledge in a small part of the model during training, so it can be removed later without affecting overall performance. It points to a new way of building AI safety and control into the training process itself. Read the paper on selective-gradient-masking for more details.

OSS Highlight of the Week

This week we are featuring Walrus. It is an open-source message streaming platform and an alternative to Kafka. Key features include high performance, automatic load balancing, fault tolerance and auto forwarding. Check the Walrus GitHub repo to know more details.

IBM is acquiring Confluent in a deal valued at $11 billion. The acquisition values Confluent at $31 per share. Confluent is known for its real time data streaming platform built on Kafka. Read the post on IBM acquiring Confluent to know more details.
Google has released the Gemini Deep Research agent for developers through the Interactions API. It uses Gemini 3 Pro to handle agentic research tasks end to end. Check Google’s blog on the Deep Research agent for more details.
OpenAI, Anthropic, and Block have formed the Agentic AI Foundation under the Linux Foundation. Each company has donated key open-source projects like OpenAI’s AGENTS.md and Anthropic’s MCP to build open standards. The goal is to create a neutral place for shared protocols and tools for AI Agents. Check Agentic AI Foundation to know more details.
OpenAI has launched its first certification courses. They focus on practical AI skills for everyday work and learning. The courses will be available through ChatGPT and Coursera. Check the announcement on OpenAI Certification Courses to know full details.
I read a post on why you should not start a new project with microservices. It shows how microservices add complexity and slow teams down. The author suggests starting with a monolith and moving to microservices only when real scaling problems appear. Check the post on -why you should never start with Microservices.

Notable FYIs

Composio has introduced Tool Router. It automatically finds the right tools in the agentic workflow. It also handles authentication and runs tools in parallel. Check Composio’s Tool Router docs to learn more details.
Orchids is a vibe coding platform with both a browser app and an IDE. It handles everything from coding to deployment. It comes with Supabase, Stripe, built in browser and other integrations. If you want to see how it works, check the Orchids vibe coding IDE page.
Unsloth has dropped new Triton kernels with auto packing. It makes fine tuning up to 3x faster while using less GPU memory and keeping model quality the same. Check Unsloth’s page to learn more about faster LLM training with Triton kernels.
Browser Use has released Skills. It lets you turn any website into a usable API with a simple text instruction. You guide it once through the site, and it creates a structured endpoint you can plug into your app. Useful if you need quick data access without custom scraping logic.
Claude Code can now fine tune models using Hugging Face Skills. Claude can write training scripts, submit jobs to GPUs, and push the trained models to Hugging Face. Check the post to see how end to end fine tuning works with Claude Code, using Hugging Face Skills.
OpenAI and Disney have made an agreement around Sora. Disney becomes the first major content licensing partner, which means you can now generate Disney characters and worlds directly in Sora. Check the OpenAI-Disney-Sora agreement for more details.

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