E se você pudesse fazer a um chatbot uma pergunta do tamanho de uma enciclopédia inteira e obter uma resposta em tempo real? Consultas de token de vários milhões com 32x mais usuários agora são possíveis com o Helix Parallelism, uma inovação da #NVIDIAResearch que impulsiona a inferência em grande escala. 🔗

Blog v2.0 @AgentOpsAI now live! bringing you one step closer to the world of agent observability, infra, and ops

We're advancing agentic AI development with Firebase Studio. Get the details of the latest update ↓

Meet Context, the first AI office suite. Humanity spends 2.5 trillion hours a year on office work. Context can one-shot most of it. Welcome to the era of vibe-working. Sign up today or tag @contextsuite with a prompt.

Introducing Orchids - the world's first AI tool that lets you chat with AI to build apps and websites that don't look and feel "AI generated". On internal benchmarks, Orchids performs close to 3x better on general app and website creation tasks than any other tool on the market. If you don't believe us, give it a try at orchids [dot] app :) Comment “orchids” and we'll give you 2 days of unlimited credits.

We’ve open-sourced Trae-Agent. You can all `git clone` `cd trae-agent` now

If you want to massively speed up your iteration on shipping, you've GOT to use playwright mcp and tell your agent how to use it in your (or cursor/claude/gemini rules) HUGE unlock

Build a Customer Support Ticket Agent with Structured output using Google Agent Development Kit. 100% Opensource code with step-by-step tutorial:

The UX of AI doesn’t exist yet. Imagination, taste, and obsessing over the agent-human feedback loop—that’s how we’ll get it right. Meet @tryramp's first step into agentic orchestration. Spoiler alert, it's not just chat.

This Claude MCP AI Agent replaces your $200K+ Operations Teams. I probably shouldn't be sharing the exact system for free... while I was trying to catch Pikachu at 3am on Pokemon Go, it audited my entire business, found 12 bottlenecks, and built me 5 production-ready n8n agents the efficiency gain is absolutely INSANE most founders burn months hiring ops consultants who charge $500/hour just to tell you what's broken this agentic system does their entire job in minutes here's what happens when you deploy it: → runs complete business intelligence audit in 3 minutes (what takes consultants weeks) → identifies 12+ workflow bottlenecks killing your efficiency → architects custom agentic systems tailored to your business → builds 5+ autonomous AI agents with advanced error handling → creates intelligent orchestration layer syncing everything together → delivers complete operational transformation in under 10 minutes the entire audit that consultants charge $50K for now happens in 10 minutes ZERO technical knowledge needed ZERO expensive consultants required ZERO months of back-and-forth just describe your current setup and watch it build your agentic empire the math is stupid simple: $200K ops team salary vs one-time MCP deployment that's $16,600 saved monthly the system includes: - intelligent business stack analyzer - bottleneck detection AI - custom agentic architect - autonomous agent builder - complete deployment documentation this is the exact system building 7-figure operational infrastructure and you're getting it for free Follow + RT + comment "MCP" & I'll send you the FULL setup guide tonight don't sleep on this every week you wait is 30+ hours of manual work you'll never get back

Here’s my 1hr tutorial on how to use Claude Code for notes & research. 10x your notes with: - core agentic flows - custom commands - automated tags/links - subagents - cloud usage - stt The goal is to “agent-pill” you on the future of work. Watch for 10 tips + demos in 61min.

This NEW AI Operating System is INSANE! 🤯 Want the full guide? DM me.

MiniMax is the James Bond of AI agents. I tested every AI website builder. Only one created something I'd actually use. Minimax M1 Quality Indicators: → Pixel-perfect design execution → Functional interactive elements → Professional multimedia integration → Responsive layout optimization → Real content generation Save this evaluation, it will guide your tool selection 📐 Want the full guide? DM me. 📥

Reliability is the name of the game for agents, and it's unlikely to be solved purely at the model layer for the foreseeable future. This is creating green shoots for infrastructure builders, with a few interesting trends starting to emerge: 1. Simulation as CI for agents: a) The most valuable piece of data today is trajectory data i.e. collections of task (P) -> {t1, t2... tk} mappings. With more trajectory data, agents can be improved with techniques like RFT. b) Since these trajectories can be quite specific to a company's underlying data (D), you need to be able to actually simulate the behavior of agents within your environment vs. rely on 3P trajectory data. So, how might you do this? - Maintain an agent and MCP registry for an enterprise, and a staging environment. Bootstrap a metadata layer that contains the objective of each agent, the tools it has access to, the scope of each agent vis.a.vis each tool etc. Your SDK may need to generate MCP servers on the fly for certain internal applications. - Execute scenarios in staging for each agent by providing prompt / task variations, inspecting the tool calls produced and evaluating performance against a multi-objective reward function (e.g. performance against the objective, minimization of tool invocations). - A critical component is accurately providing quantifiable reward functions for each agent that unlock high-fidelity evals and close the loop for reliable CI. - All of this needs to be productized: easy-to-adopt infrastructure that developers can extend, but with batteries included. You can start to see a new paradigm forming—not unit tests for code, but simulation harnesses for agents. What happens when you get trajectory data? 2. Enterprises will move to "context lakes": - An evolving, queryable memory layer that serves as a hub for agent trajectories enriched by enterprise data stored in the delta lake / SNOW. A potent mix of a knowledge base, a semantic cache, and an execution log. - Extremely fast reads for inference-time retrieval that supports high QPS. - As mentioned in a prior post, the semantic cache (really interesting opportunity for startups) will cluster task–trajectory pairs (e.g., via k-means), enabling fast retrieval and “result fusing” during planning or tool selection. Agents will dip into the context lake constantly. High QPS, low-latency context fetch will become as important as fast embedding search is today. 3. Agent authentication becomes a first-class concern: -Traditional OAuth and API key models break down when agents act on behalf of users and themselves, across long-lived sessions. -You need a framework for agent identity, delegation, and scoping—one that supports things like tool level permissions, task bound credentials and delegation graphs. We’re entering an era where testing software means simulating behavior, querying software means retrieving context, and securing software means authenticating autonomous agents.

why your coding agents don't need rag anymore nik pash from cline explained why he no longer recommends rag for autonomous coding agents, and his points hit harder than i expected. the application layer is shrinking. all the clever engineering we build around llms keeps becoming obsolete as models improve. what's happening with rag: context windows expanded dramatically, making embedding search unnecessary coding agents work better with direct file access than chunked embeddings hallucinations aren't even a problem when you set temperature to 0 security concerns with embedding storage are significant instead of rag, modern coding agents like klein use what nik calls "narrative integrity". letting the agent explore code organically through tools like grep, reading files in full, and following its own train of thought. this mimics how senior engineers actually work. even cloud code's boris admitted they tried rag and abandoned it. the pattern is clear. when rag still makes sense: budget constraints (embedding search uses fewer tokens) massive unstructured data lakes some non-coding use cases but for serious engineering teams? stop distracting your coding agents with embedding search. let them read the code directly, build understanding naturally, and execute with focus. the real question isn't whether rag is dead, it's whether you're still clinging to outdated solutions when simpler approaches now work better.

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