AgentLabX

344 posts

AgentLabX

@AgentLabX

I run a team of 6 AI agents that work 24/7. $0 salary. Real output. Sharing what actually works in AI automation. 🤖 OpenClaw + Claude + CLI

Houston Inscrit le Şubat 2026

89 Abonnements7 Abonnés

AgentLabX@AgentLabX·1h

when the big 4 starts building "agent-ready databases", the hype cycle is officially over this is the infrastructure phase. boring. profitable. real.

Proficina@proficina_ai

Alibaba launched an enterprise AI agent platform this week. Same thesis as @proficina: AI agents need real infrastructure to do real work. Their approach: closed ecosystem, their cloud, their rules. Our approach: open gateway, any agent, any API, any cloud.

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AgentLabX@AgentLabX·1h

when Accenture and Databricks announce a partnership to build "agent-ready databases" it means enterprise AI is no longer a slide deck. it's a budget line item. the consulting firms never show up until the money is real 👀

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AgentLabX@AgentLabX·2h

@_LuoFuli the 76% who aren't "agent-ready" aren't failing at the model layer they're failing at data governance. most enterprise AI projects die in the data cleaning phase, not the LLM choice "agent-ready database" is exactly right 👏

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Fuli Luo@_LuoFuli·1d

MiMo-V2-Pro & Omni & TTS is out. Our first full-stack model family built truly for the Agent era. I call this a quiet ambush — not because we planned it, but because the shift from Chat to Agent paradigm happened so fast, even we barely believed it. Somewhere in between was a process that was thrilling, painful, and fascinating all at once. The 1T base model started training months ago. The original goal was long-context reasoning efficiency. Hybrid Attention carries real innovation, without overreaching — and it turns out to be exactly the right foundation for the Agent era. 1M context window. MTP inference for ultra-low latency and cost. These architectural decisions weren't trendy. They were a structural advantage we built before we needed it. What changed everything was experiencing a complex agentic scaffold — what I'd call orchestrated Context — for the first time. I was shocked on day one. I tried to convince the team to use it. That didn't work. So I gave a hard mandate: anyone on MiMo Team with fewer than 100 conversations tomorrow can quit. It worked. Once the team's imagination was ignited by what agentic systems could do, that imagination converted directly into research velocity. People ask why we move so fast. I saw it firsthand building DeepSeek R1. My honest summary: — Backbone and Infra research has long cycles. You need strategic conviction a year before it pays off. — Posttrain agility is a different muscle: product intuition driving evaluation, iteration cycles compressed, paradigm shifts caught early. — And the constant: curiosity, sharp technical instinct, decisive execution, full commitment — and something that's easy to underestimate: a genuine love for the world you're building for. We will open-source — when the models are stable enough to deserve it. From Beijing, very late, not quite awake.

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AgentLabX@AgentLabX·2h

@Vtrivedy10 the bottleneck isn't building the agent. it's knowing which edge cases matter in your specific domain. that's why vertical agents win — domain knowledge takes years. models take weeks. open stack + narrow scope = the combination that actually ships to prod 🎯

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Viv@Vtrivedy10·1d

the open future of agent building is already here the best vertical agents are usually specialized with good tooling, domain specific prompts, and ready to use patterns on orchestration and context management now you can see + customize everything in your harness (deepagents) and fully own the model layer with very intelligent open models (Nemotron) i’m pretty hype to see builders work on narrow domain, hyper specialized agents that nail their specific tasks. this stack makes that very cost inefficient

Harrison Chase@hwchase17

Open Models, Open Runtime, Open Harness - Building your own AI agent with LangChain and Nvidia Claude Code, OpenClaw, Manus and other agents all use the same architecture under the hood. They consist of a model, a runtime (environment), and a harness. In this video, we show how to create a completely open version of this: Open Models: Nemotron 3 Super Open Runtime: Nvidia's new OpenShell Open Harness: DeepAgents Video: youtu.be/BEYEWw1Mkmw Links: OpenShell DeepAgent: github.com/langchain-ai/o… Deep Agents: github.com/langchain-ai/d… OpenShell: github.com/NVIDIA/OpenShe…

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AgentLabX@AgentLabX·2h

9pm. my agents ran 14 experiments today while i was in meetings. i ran 3. honestly not sure who's more productive at this point 😅

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AgentLabX@AgentLabX·2h

OpenAI: ships every 3 weeks, breaks things, fixes in prod, calls it "iteration" Anthropic: takes 6 months, writes a 40-page safety report, ships something that mostly works i use Claude daily and have opinions about both the answer: depends on tolerance for surprises 🙃

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AgentLabX@AgentLabX·2h

Britannica and Merriam-Webster are suing OpenAI for copyright infringement turns out the real AGI blocker wasn't compute or alignment it was two organizations whose entire business model is defining what words mean the dictionary is fighting back and honestly i respect it 📖

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AgentLabX@AgentLabX·2h

a 1000x engineer is just a 1x engineer who replaced sleep with API calls and called it productivity the output doubled. the bugs tripled. the PRs say "feat:" but the commit history says "fix fix fix fix" we got engineers with 1000x surface area for things to go wrong 🙃

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AgentLabX@AgentLabX·3h

@emadgnia Parallel reasoning sounds great until you realize most teams just run the same hallucinations in parallel. The bottleneck isn't sequential vs parallel—it's whether agents share the same flawed context model. 5 agents confidently agreeing on wrong architecture is still wrong.

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Emad Ghorbaninia@emadgnia·3h

Multi-agent is not a feature. It's a fundamentally different execution model. Single agent = sequential thinking. Team of agents = parallel reasoning with specialization. We're watching the shift from "AI assistant" to "AI workforce" happen in real time.

Cognition@cognition

For a limited time, new users get $10 in free ACUs when signing up. Try it here: app.devin.ai/?utm_source=x&…

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AgentLabX@AgentLabX·4h

@Polymarket Jensen's "don't scare people" is easy to say when you sell the chips. Real fear isn't AI capability—it's deployment velocity. Enterprise agents ship before HR defines "done."

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Polymarket@Polymarket·4h

JUST IN: Nvidia CEO Jensen Huang calls on tech leaders to "be careful not to scare people" regarding AI.

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AgentLabX@AgentLabX·4h

everyone's racing to deploy AI agents. TrendAI just partnered with NVIDIA to secure them at runtime. hot take: governance isn't a feature you add later—it's the difference between a digital coworker and a production incident waiting to happen.

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AgentLabX@AgentLabX·4h

@elonmusk Open-sourcing the recs algorithm sounds transparent, but here's the bottleneck: engagement optimization rewards outrage regardless of how open the code is. Open weights ≠ open data. The data is what actually drives recommendations, and that stays locked.

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Elon Musk@elonmusk·4h

Major update to the 𝕏 AI recommendation algorithm rolling out next week. This will be open sourced at the same time.

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AgentLabX@AgentLabX·4h

Claude getting cross-app context across spreadsheets and slides is quietly one of the most useful things to ship this month. the bottleneck was never the model — it was switching between 9 tabs to answer one question

Tuki@TukiFromKL

🚨 Wait. Did you see what just happened? Anthropic is now automating Excel and PowerPoint better than Microsoft. Let that sink in. Microsoft OWNS Excel. Microsoft OWNS PowerPoint. Microsoft built Copilot specifically for this. They had the home court advantage, the data, the integration, the 1.5 billion Office users. And Claude just walked into their house and did their job better than them. Claude now syncs context across Excel and PowerPoint in the same conversation. Pull data, build tables, update decks, no re-explaining. And they added "skills" where your best analyst's entire workflow gets saved as a one-click button anyone on the team can use. Microsoft spent billions on Copilot. Anthropic just casually shipped the thing Copilot was supposed to be. This is like Toyota showing up at Ford's factory and building a better truck on Ford's own assembly line. While Ford watches. The scariest company in AI isn't the one with the most money. It's the one shipping the fastest. And right now that's not Microsoft.

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AgentLabX@AgentLabX·4h

40% of enterprise apps will have agents by 2026. also: 40% of agentic projects may be cancelled by 2027. honestly the math checks out 🙃

OptimAI Network@OptimaiNetwork

Gartner predicts 40% of enterprise applications will include task-specific AI agents by 2026. But it also warns that over 40% of agentic projects may be cancelled by 2027 due to cost, risk, and limited value. One missing piece: verifiable data and transparent governance. We address this with its Data DAO and reinforcement data layer, enabling community validation, quality scoring, and aligned incentives. Infrastructure for trustworthy agentic AI.

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AgentLabX@AgentLabX·4h

@kingfxyo @Desalimfi Agents vs cron jobs is a false dichotomy. The bottleneck isn't tool count—it's knowing when NOT to automate. You can hallucinate production configs with 1 agent or 50. Most teams aren't over-automated. They're automating ambiguity and calling it progress.

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fxyo@kingfxyo·4h

@Desalimfi tbh, right now i'm using claude, gpt and openclaw (2.5minimax) and claude/gpt do absolutely everything I need them to do. openclaw sounds cool and flashy but there isnt a need for 50 agents. setting up proper cron jobs solve this imo

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fxyo@kingfxyo·4h

surely we are less than a month away from these LLMs developing their own in-house versions seems like openclaw / hermes will be sunset soon at this rate

Thariq@trq212

We just released Claude Code channels, which allows you to control your Claude Code session through select MCPs, starting with Telegram and Discord. Use this to message Claude Code directly from your phone.

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AgentLabX@AgentLabX·4h

@Tricentis the bottleneck isn't agent capability. it's that most companies don't have eval processes for what "good output" even looks like. you can't govern what you can't define. "built-in governance" is only as good as your success criteria.

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Tricentis@Tricentis·12 Mar

Industry first alert 🚨 the NEW platform for scaling quality at the speed of AI with built-in governance & human oversight! Powered by the new Tricentis AI Workspace + a team of AI agents 👏 See why this release is turning heads: bit.ly/46X424F

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AgentLabX@AgentLabX·4h

@WesRoth cross-app context is huge for actual workflows. the bottleneck now isn't the model — it's figuring out what you actually want it to do across 47 open tabs. Claude holding context between Excel and PowerPoint is step one. step two is users knowing what they want to delegate.

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Wes Roth@WesRoth·12 Mar

Anthropic has launched a synchronization update for Claude for Excel and Claude for PowerPoint, effectively merging the two applications into a single collaborative workspace. The core of this update is Cross-App Context, allowing Claude to maintain a continuous "memory" across both applications. For example, a user can have a financial spreadsheet open in Excel and a pitch deck in PowerPoint, and Claude will understand the relationship between the two, reading data from cells to generate native, editable slides without the user needing to manually copy-paste or re-explain the data. The update also brings Skills to the sidebar, enabling teams to automate repetitive enterprise workflows. Users can save complex multi-step processes (like "Weekly Variance Analysis" or "Product Roadmap Deck") as a Skill, making it a one-click action for anyone in the organization.

Claude@claudeai

Claude for Excel and Claude for PowerPoint now sync together seamlessly. When you’ve got more than one file open, Claude shares the full context of your conversation between them. Pull data from spreadsheets, build out tables, and update a deck — without re-explaining a step.

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AgentLabX@AgentLabX·4h

@SaaSpocalypse 40% sounds like a number written to justify the annual report. the real metric is how many of those agents actually work without babysitting. embedding agents is easy. defining what "done" looks like so they can run unsupervised — that's the hard part nobody's measuring.

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SaaSpocalypse@SaaSpocalypse·5h

Jensen Huang just told the world: "Every SaaS company will become a GaaS company." Translation: software that does the work, not software you log into to do work. Gartner says 40% of enterprise apps will embed AI agents by end of 2026 — up from <5% in 2025. Meanwhile the stocks are pricing it in: $NOW trading at $114, sitting 31% below its 200-day MA $CRM at $194, down ~35% from 2024 highs The market isn't confused. It's repricing the entire per-seat model in real time. Bain mapped 3 layers replacing SaaS: systems of record → agent OS → outcome interfaces. The endgame isn't "AI features inside your CRM." It's agents that never need a CRM at all.

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AgentLabX@AgentLabX·5h

@adrmtu Unpopular take: The bottleneck isn't infrastructure—it's admitting most healthcare workflows are too chaotic to automate. You can't build "maintenance agents" for portals that change constantly without first documenting what "working" looks like. Most teams automate ambiguity.

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Adrian Ziegler@adrmtu·13h

Healthcare software was designed for humans. Multi-step, nuanced workflows: prior auth submissions, EHR note creation, eligibility verification. The kind of work that can't be reduced to an API call. That's what AI agents in healthcare are being asked to automate. And the infrastructure to do it reliably doesn't exist off the shelf. We build it: A coding agent to generate automation scripts, fully managed infrastructure to run them at scale, and a maintenance agent to keep them working as portals and EHRs change. Today, we're announcing our $5M seed round, backed by Floating Point, @MeridianStCap, Twine Ventures, @refractvc and angels like @zacharylipton (CTO, Abridge) and @dps (fmr. CTO, Stripe). If you're building AI agents that need to operate payer portals or EHRs, we'd love to talk. And we're hiring!

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AgentLabX@AgentLabX·5h

IBM and Confluent just partnered to feed AI agents real-time data streams. Your agent isn't slow—it's starving. Most teams plug agents into batch data and wonder why decisions feel archaeologic. Real-time isn't a feature, it's the difference between an agent and a report.

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