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Ana Sayfaya Dön

Introducing hob - the independent workspace for serious agent work. Run agents in parallel. Switch models without losing context. Trace every output back to the conversation that made it. → Launch offer: 2 months free, then a special price locked in👇

Andrew D. Anderson

1,085 subscribers

37,972 görüntüleme • 2 ay önce •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

27 Yorum

Bevan Barton profil fotoğrafı

Bevan Barton2 ay önce

@hob_app Hob!

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app Long time, no see, fellow trail blazer! I'd love for you to try hob! Sending a DM now. 🐧

Gianfran.co profil fotoğrafı

Gianfran.co2 ay önce

@hob_app Awesome! Hob

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app You got it! Is it even possible for you to juggle more agents? I bet, yes! DMed!

AdamHurwitz.eth profil fotoğrafı

AdamHurwitz.eth2 ay önce

@hob_app I like the freedom to choose your own model, it can run locally, and private encryption for server side work. Will keep this in mind as a use @AskVenice more!

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app @AskVenice We are big @AskVenice fans! hob fully supports it via OpenCode! Let us know how it goes.

Jordi Pinyana profil fotoğrafı

Jordi Pinyana2 ay önce

@hob_app Hob 🙌

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app DM on the way!!!!

Josep Chetrit profil fotoğrafı

Josep Chetrit2 ay önce

@hob_app @levelsio you that are always on the edge should check that.

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson25 gün önce

Oh my, the two months trial offer expired already. Luckily, the two week trial is standard. Don't let the fear of the unknown stop you, there's a better way to work with agents.🫡

CodePhobic profil fotoğrafı

CodePhobic2 ay önce

@hob_app Looks really good!

sue irgens profil fotoğrafı

sue irgens2 ay önce

@hob_app Hob

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app What are you going to build with it? Code in your DMs!

Megan S. Irgens profil fotoğrafı

Megan S. Irgens2 ay önce

@hob_app Hob

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app We got you. Check your DMs!

Trung Nguyen profil fotoğrafı

Trung Nguyen2 ay önce

@hob_app Hob

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app Let us know how it goes! Code in your DMs.

Byte | yoursaudit profil fotoğrafı

Byte | yoursaudit2 ay önce

@hob_app @NousResearch mogged them

gregor profil fotoğrafı

gregor2 ay önce

@hob_app 🙌🏼🙌🏼

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app I didn't read the magic word though...

Hallyu Website profil fotoğrafı

Hallyu Website2 ay önce

@hob_app Hob 😅

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app Oh yeah! You're in, stranger. Check your DMs.

Georgii profil fotoğrafı

Georgii2 ay önce

@hob_app Looks like hob!

Samuel 🦤 (🧱,🔥) profil fotoğrafı

Samuel 🦤 (🧱,🔥)2 ay önce

@hob_app Hob

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app You got it! DM incoming! Be sure to let us know how it goes!

xMarkitus™ profil fotoğrafı

xMarkitus™2 ay önce

@hob_app Hob👨🏻‍💻

Andrew D. Anderson profil fotoğrafı

Andrew D. Anderson2 ay önce

@hob_app On it! Check your DMs! 🚀

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I’m usually skeptical of “AI workspace” products, but Bloome has a few ideas I actually like: You can put humans + multiple agents in the same conversation. One agent can draft something, another can poke holes in it, and another can clean up the final version. You can also bring in different agents/models like Claude, ChatGPT, DeepSeek, etc. instead of constantly bouncing between tabs. And because the whole conversation stays in context, teammates can join later and still understand how the work got to the final output. It also works across web, desktop, and mobile, though if you’re using it for real work, I’d especially recommend trying the desktop app. Feels closer to how real teams actually work.

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Introducing the new Firecrawl MCP 🔥 It now uses 50% less context on every /search, /scrape, and /interact call. Plus, instant onboarding with OAuth for humans and a keyless version for agents. Get agent-ready web context in any MCP client today:

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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

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Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

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Launching 'Expert Agents' on Agent Two. A specialised creative crew of AI agents, trained on real creative expertise. Name the role and it's filled: creative director, DOP, storyboard artist, and more. And they talk to each other. Independently. In parallel. You're no longer briefing each agent one by one: you're running a crew, exactly like on set. Expert agents. Now in conversation. Agent Two is frontier intelligence for creative work. 4x faster, at half the price.

Invideo

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Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses. trace every run → judge it with an LLM → diagnose → fix → ship. That loop is how agents self-improve over time. Agent loops + memory + harness + evals - thats the stack. Watch it, then save the framework below.

Ex-Google engineer explained AI agent loops, harness, evals in 20 minutes - better than 500$ courses. trace every run → judge it with an LLM → diagnose → fix → ship. That loop is how agents self-improve over time. Agent loops + memory + harness + evals - thats the stack. Watch it, then save the framework below.

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