What if your coding agents never needed an internet connection? 🌐❌Google just made this possible by bringing Gemma 4 entirely offline via the Google Antigravity SDK and LiteRT.By keeping sensitive code strictly on-device, eliminating token costs, and plugging directly into environments like Ollama or LM Studio, the friction of building secure agentic workflows is completely gone.Looking at the benchmark, running a 26B model locally while maintaining zero-token expenditure is impressive engineering.Are you looking to migrate your development workflows to local models this year? Let's discuss below! 👇

My verdict after reading docs, local install, setup, config: - difficult to begin (this is a major pitfall, IMO) - the lack of a clear coding harness - the effort is much higher in comparison with a widely used LangGraph So: NO, this is not an IDE, it is just an SDK. Continue/Cline/Other plugins will do the same functionality out of the box as a coding harness. LangGraph supports a larger number of integrations. YES, this is a step forward for those like (or should) to use Google SDK/ADKs.

My feed is in full boss‑battle mode — Google dropping offline LLM gauntlets while OpenAI pushes Work surfaces. 2026 is wild.

The privacy story in AI agents is moving onto the machine. Tucky keeps notes encrypted in a local-only file while its agent searches and writes across Mac apps. Airuncode runs coding agents on the developer’s own computer and accepts voice tasks. AI Toolbox keeps chat content in the browser and syncs only encrypted folders, prompts, and tags. Local execution is becoming a product feature, not a footnote in the settings menu. Full analysis in the first reply ↓

My weekend find — Obscura. A totally open-source headless browser for AI agents. For personal use, I try to utilize free credits from different vendors as much as possible. I noticed that doing research using an agent that involves going through websites takes a lot of time and consumes a lot of tokens. So I started reading about headless browsers with good MCP compatibility, and I found Obscura. I installed it via Apple Container (you can use Docker as well): docker run -d --name obscura -p 9222:9222 h4ckf0r0day/obscura Then I added its MCP server to my Antigravity client and voila! Now I can run browser-related activities from my agent without actually opening any browser tabs. It has pretty advanced browser features as well. I’m currently testing it, so I’ll keep exploring its pros and cons. But so far, it seems pretty promising. I even created a small video of it doing basic scraping to generate analytics. #AI #headlessbrowser #mcp

@alohabrowser launched AlohaJet, an agent-first browser aimed at cheaper web automation. Deep View covered it Sep 22. Project: https://lnkd.in/eG6Y3s4Y Open test. Free download. Apple Silicon Mac. Drives Claude Code or Codex on your machine. They cut page-read waste so agents spend fewer tokens. I built a full local Chromium with AI in the product, not bolted on as an extension. Human-action mode for socials and bot-detect sites. Fast mode uses TypeSafe Jev for quicker navigation decisions. Full page access for the agent. #AIBrowser #BrowserAutomation #AIAgents #MurableBrowser #AgenticAI

TypeSafe AI released Jev and I got access to it. It does not generate text, so we can't use it for coding. You hand it a state and a fixed set of options, and it gives back a probability for each one. That shape fits a few things like: search "the command I used to fix docker networking" over my shell history, the same over browser history, ticket triaging: labels and priority, model routing etc. Before using it, I wanted to check its speed and performance against the frontier models we use every day. So I made it play Battleship. I was pretty convinced it would lose one sided. But surprisingly, it was very close. Sonnet won the best of five, 3-2. But over those same five games Jev ran at 388 ms a move against Sonnet's 1,297, and cost $0.029 against $1.72. I would not hand it a hard reasoning problem. For a tight loop over a known option list that has to be instant and cheap, it is a real candidate. Play against Jev: https://lnkd.in/d5QP2ATQ Longer comparison: https://lnkd.in/dVUwWcVq

Melaya BROWSER EXTENSION IS NOW LIVE - STILL MODEL FREE Most "AI browser agents" quietly launch a second browser you cannot see, log into throwaway accounts, and act where you have no visibility. That is convenient for a demo and wrong for real work. ⠀ I built Melaya's extension the other way, and it is now live on Chrome and Edge. ⠀ The decision I want to explain is why it attaches to ONE tab you pick, instead of roaming your whole browser. The easy version is to let the agent enumerate and switch tabs freely. It looks more powerful in a video. It is also how an agent ends up posting from the wrong account or acting on a page you never meant to share. ⠀ So the model can never choose the target. A trusted picker you control mints a short-lived, single-use grant for exactly one tab. The agent reads that page, and every consequential action is designed to stop for your approve, edit, or reject decision. The reads run in the page itself, which is why it stays fast without shipping your DOM somewhere else. ⠀ The harder engineering was making it honest under pressure. When the tab hangs or a page fights automation, the safe answer is to fail closed and tell you, not to guess and click. That is a worse demo and a better product. ⠀ If you build agent products: the useful question is not "how many things can it touch", it is "who chooses the target, and who confirms the write". Those two answers decide whether an operator can actually trust it. ⠀ I would like to hear from people who have shipped browser automation in production: where did unbounded tab access bite you, and what did you change? ⠀ Chrome: https://lnkd.in/euGfR6Ev Edge: https://lnkd.in/eV36pYFF https://lnkd.in/egDU9NAz #AIAgents #AgenticAI #AIGovernance #BuildInPublic #BrowserAutomation

The AI release cycle is getting wild. 3 Flash models in 6 weeks and Google is clearly optimizing for agents, not just benchmarks. Tunable reasoning + lower cost = more real products will actually ship.

How can AI make the world a better place? I prototyped a tool that answers "Is this video OK for a young kid?” on YouTube in real time. It costs ~$.60 per 1000 videos with Jev, YouTube API, and MobileCLIP. That’s $.00007 per video at median speed of .66 seconds for evaluation. Chrome extension ➡️ local server ➡️ YouTube API for metadata ➕ MobileCLIP w/ a custom trained classifier for thumbnail review ➡️ JEV (TypeSafe's System One model) for the decision. Built with Claude Fable 5.1 Low in about 4 hours.  Limitations: Runs locally on my Mac. Chrome extension (web only). API calls. Rubric is based on my preferences.  Possibilities: 🤯