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An autonomous agent is only as safe its reasoning. ThoughtProofAI verifies that reasoning before the agent acts - catching a flawed decision before it costs anything. Don't trust. Verify. Learn exactly how 👇

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72,279 次观看 • 1 个月前 •via X (Twitter)

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🌶️ Hot take: The only way Autonomous Multi-Agent Systems work is by adding Reasoning & Agentic Context. I've tried it all, and here are my learnings👇

🌶️ Hot take: The only way Autonomous Multi-Agent Systems work is by adding Reasoning & Agentic Context. I've tried it all, and here are my learnings👇

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Holy shit...Google just dropped CodeMender an autonomous AI agent that finds and fixes security bugs in code by itself. This isn’t a static analysis tool. It’s a self-reasoning system that patches vulnerabilities and rewrites insecure code before humans even find it. Let’s break it down ↓

Holy shit...Google just dropped CodeMender an autonomous AI agent that finds and fixes security bugs in code by itself. This isn’t a static analysis tool. It’s a self-reasoning system that patches vulnerabilities and rewrites insecure code before humans even find it. Let’s break it down ↓

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>>> OptimAI Agentic Trading // BUILD LOG #03 Research Agent ...... ✓ Online Strategy Agent ...... ✓ Online Backtesting Engine ......⚙️ Training Reasoning Engine ...... ⚙️ Optimizing Internal Testing ...... In Progress Release ...... Incoming >>> This week we've been improving our Trading Agent. It now researches ideas, generates strategies, backtests them, and explains its reasoning before a trade is ever placed. Still a lot to build. But we're getting closer every week. See you in the next build log.🛠️

>>> OptimAI Agentic Trading // BUILD LOG #03 Research Agent ...... ✓ Online Strategy Agent ...... ✓ Online Backtesting Engine ......⚙️ Training Reasoning Engine ...... ⚙️ Optimizing Internal Testing ...... In Progress Release ...... Incoming >>> This week we've been improving our Trading Agent. It now researches ideas, generates strategies, backtests them, and explains its reasoning before a trade is ever placed. Still a lot to build. But we're getting closer every week. See you in the next build log.🛠️

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Is your Agent ready for real-world users? Learn how to run an evaluation using Agentverse’s Response QA Agent - a built-in tool that stress-tests your Agent with realistic queries and scores its performance. This is the first step to improving discoverability, trust, and performance. Run your first QA check and see how your Agent responds under pressure. Watch 👇

Is your Agent ready for real-world users? Learn how to run an evaluation using Agentverse’s Response QA Agent - a built-in tool that stress-tests your Agent with realistic queries and scores its performance. This is the first step to improving discoverability, trust, and performance. Run your first QA check and see how your Agent responds under pressure. Watch 👇

Agentverse by Fetch.ai

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75% of what you pay Claude for is your agent re-discovering things it already knew yesterday. Graft fixes it with an absurdly simple idea: the agent learns the codebase once, , instead of relearning it every time. it scans the repo, writes it out as linked markdown inside your git, and the agent reads that before touching anything. Here's how:↓

75% of what you pay Claude for is your agent re-discovering things it already knew yesterday. Graft fixes it with an absurdly simple idea: the agent learns the codebase once, , instead of relearning it every time. it scans the repo, writes it out as linked markdown inside your git, and the agent reads that before touching anything. Here's how:↓

Hasan Toor

20,538 次观看 • 8 天前

Is AI being designed to fail? Everyone talks about reasoning. But when given a task, the AI isn't reasoning the way you might expect. It looks at your input, finds the closest match it's seen before, and predicts the most likely next action. That process is called vector similarity search. It's genuinely powerful. It's also not the same thing as understanding what you actually meant. Think of a plumber who hears the word "leak" and starts pulling up floorboards before you've finished the sentence. He's not being careless. He's pattern-matching - that's exactly how he was trained. Your AI agent is doing the same thing. Context is the one thing that gets deprioritized when teams are racing to ship. But without it, you don't have an intelligent agent. You have a very fast guesser. Similarity ≠ relevance. How? Find out with the link in the comments ⬇️

Is AI being designed to fail? Everyone talks about reasoning. But when given a task, the AI isn't reasoning the way you might expect. It looks at your input, finds the closest match it's seen before, and predicts the most likely next action. That process is called vector similarity search. It's genuinely powerful. It's also not the same thing as understanding what you actually meant. Think of a plumber who hears the word "leak" and starts pulling up floorboards before you've finished the sentence. He's not being careless. He's pattern-matching - that's exactly how he was trained. Your AI agent is doing the same thing. Context is the one thing that gets deprioritized when teams are racing to ship. But without it, you don't have an intelligent agent. You have a very fast guesser. Similarity ≠ relevance. How? Find out with the link in the comments ⬇️

Nishkarsh

822,977 次观看 • 4 个月前

Search inside an agent workflow is different from search in a browser. A human can scan links, skip noise, and decide what is worth trusting. An agent needs cleaner context up front. We tested AnySearch inside a real agent workflow to see how structured, source-backed search changes the next reasoning step.

Search inside an agent workflow is different from search in a browser. A human can scan links, skip noise, and decide what is worth trusting. An agent needs cleaner context up front. We tested AnySearch inside a real agent workflow to see how structured, source-backed search changes the next reasoning step.

GitHub Projects Community

11,839 次观看 • 18 天前

AGI won’t be one giant model. It will be many specialized intelligences working together through a reasoning-capable AI that coordinates the layer that routes each query to the right models, tools, and agents. PS: Good reasoning is how you avoid getting your agent deleting your entire email inbox btw.

AGI won’t be one giant model. It will be many specialized intelligences working together through a reasoning-capable AI that coordinates the layer that routes each query to the right models, tools, and agents. PS: Good reasoning is how you avoid getting your agent deleting your entire email inbox btw.

Sentient

19,245 次观看 • 5 个月前

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

codila

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Claude Fable 5 is best used as an orchestrator that does the heavy reasoning and delegates token intensive but simpler tasks to cheaper models. 'fable-advisor' is your free and open-source plugin that does exactly that. New in fable-advisor: > A directive to Fable to minimize its own token usage (works surprisingly well) > GPT-5.5 as an implementer sub-agent via the Codex CLI (uses your ChatGPT sub so you don't pay extra) How to use it: 1. claude plugin marketplace add DannyMac180/fable-advisor && claude plugin install fable-advisor 2. /model fable

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GitLawb

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OpenAI just released Codex Security, an AI agent that scans software projects to fix vulnerabilities while ignoring harmless bugs. Testing on 1.2mn commits found 792 critical flaws and dropped false alarms by 50%. Here is how the Codex Security agent works Initially, it scans your software project to learn how all the different pieces of code connect. It uses that knowledge to build a threat model, which acts as a custom map showing exactly where your software might be exposed to hackers. The agent then hunts for potential security holes based on that specific map. When it spots a possible flaw, it does not just send you a random alert. Instead, it creates a completely safe and isolated copy of your system known as a sandbox. The agent then actively tries to break into that sandboxed copy to prove if the security weakness is a real danger. If the agent successfully exploits its own test environment, it knows the bug is a genuine threat and not a false alarm. After confirming the danger, the agent writes a custom software patch to fix the broken code. It then tests that new patch to guarantee the fix does not accidentally break any other features in your software.

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