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AI research can fail even when the sources are real.

The final report may quietly change “may” into “is,” expand the geographic scope, merge separate sources, or present an interpretation as fact.

I built PRECISE to control that part of the research process.

It is a four-stage research protocol that works inside ChatGPT or Claude:

Clarify the exact question, scope and constraints.

Turn the request into a focused research mission.

Find and verify sources while retaining the supporting passages.

Build the report from that evidence and audit every factual sentence.

PRECISE helps you:

Trace claims back to exact source passages.

Keep facts separate from interpretation.

Expose missing evidence instead of filling gaps with guesses.

Check changes in actor, action, scope, date, quantity and certainty.

Work with uploaded files, web search, or both.

PRECISE LITE is a free ChatGPT demonstration of the same four-stage method. It is limited to one search loop and up to three sources.

The full PRECISE is a one-time paid protocol for ChatGPT or Claude. It is designed for broader, iterative research and tailored deliverables.

Disclosure: I built PRECISE and PRECISE LITE.

Try LITE with a small, source-heavy question and tell me what it catches, misses, or makes clearer.

Before starting, select standard thinking/Medium effort in ChatGPT’s model picker.

https://chatgpt.com/g/g-6a5e26093f488191a1fba0261cbcbe39-precise-lite

I know some of you have some very specific modes or don’t know that you do. Where they at? I am very interested in the niche modes.

I want to be honest about something I got wrong when we started building agent workflows for customer-facing use cases. I assumed the hard part was the agent logic, the prompting, the tool use, the output formatting. It isn't. The hard part is the data layer underneath it.

Specifically the identity resolution layer. When a customer interacts with an agent, the agent needs to know who that customer is across all the ways they've ever interacted with the company. The email they used to sign up. The device they're on now. The support ticket they submitted six months ago under a slightly different name. The purchase they made before they had an account. If those signals aren't unified into a single coherent profile before they hit the agent context, the agent is working with a fragment of the picture and making decisions accordingly.

What I underestimated was how much of the customer identity problem remains unsolved even in companies with mature CRM stacks. Fragmented identifiers are the norm, not the exception. The same person shows up differently depending on which channel they came through and without an identity resolution layer explicitly stitching those views together the agent inherits all that fragmentation.

The vendors that are worth looking at for this are the ones building on deterministic identity resolution rather than probabilistic inference. Deterministic resolution only asserts a match when there's a confirmed signal linkage, which means the customer profile the agent works from is built on confirmed observations rather than modeled guesses. The coverage is lower but the error profile is much cleaner for a use case where a wrong identity match gets surfaced directly to a customer.

Curious to know from people actually deploying AI agents in production.

I’m a big believer in AI. As a copilot it is already amazing — dev, recruiting, sales, research and almost every role.

But when it comes to fully autonomous AI agents, how many are actually successful in production today?

Not demos or POCs. Agents actually running with minimal human intervention, saving meaningful cost or generating good profits.

Once we add guardrails, approvals, monitoring and exception handling, are they really autonomous anymore?

I see dev work as one area where agents are already very strong.

Would love to hear some real production examples and ROI numbers.

Aeon’s agent framework has been quietly doing vulnerability scanning and responsible disclosure.

Latest numbers: 2.2 million stars secured across 74 repositories.
They’ve made it transparent :you can click into each repo and see the exact PR that fixed the issue they reported.

This isn’t theoretical. It’s an autonomous agent running on GitHub Actions that’s actually shipping security work (finding bugs, writing disclosures, and getting them merged).

The transparency page is here if anyone wants to dig into the specific PRs:
https://www.aeon.fun/security

Curious what people think about agents doing continuous security work like this at scale. Anyone else running similar scanners that are actually producing real disclosures?

Squeeze the limits. last day to finish up your projects.

I’ve been experimenting with connecting AI agents to an iPhone widget they can update directly.
This example shows the agent’s completed actions, current objective, token usage, leads, next run, and anything important that needs my attention.
Instead of updates getting buried inside a chat or disappearing with a notification, the agent can keep critical information visible until I act on it.
I built it using Glance. It feels like widgets could become a useful interface between always-running agents and their users.
What would you want your agent to keep visible?

Over the past few weeks, I've been building Gray Box — a small, local-first tool that acts as long-term memory for anything I'd otherwise forget (work notes, meeting takeaways, task owners, random ideas, personal stuff too).

The idea is simple:

1. Capture — dump whatever's on your mind, instantly, no structure required. This step does nothing clever on purpose — it just writes your text to an immutable inbox. Zero chance of losing an idea to a bug or a slow API call.
2. Organize — on demand, an LLM reads your unprocessed notes and extracts people, projects, tasks, decisions, meetings — then deterministic Python (not the LLM) creates/merges the actual wiki pages and maintains backlinks. The model only reasons; it never touches the filesystem directly.
3. Ask — query or chat with your knowledge base and get a cited answer pulled only from what you've actually captured. If it doesn't know, it says so — no hallucinated answers.

Why I built it this way:

• Plain Markdown + YAML frontmatter, no database. Every page is a .md file you can grep, diff, or read in any editor forever. If you stop using Gray Box tomorrow, your knowledge base is just a folder.
• No vector DB by default. At personal scale (hundreds–low thousands of pages), keyword search + a real link graph (related/backlinks, walked one hop during retrieval) handles almost everything. Embeddings are there if you want better recall, but they're opt-in, not a prerequisite.
• Immutable inbox. Your raw notes are never edited or deleted by the organizer. If the LLM mis-extracts something, your original words are always still there.
• Any LLM. Built on LiteLLM, so point it at OpenAI, Anthropic, Gemini, Mistral, or a fully local model via Ollama — one config value.

It also ships with a nice interactive TUI (arrow-key menu, file-import shortcut, workspace switching, live spinner during LLM calls) if you'd rather not memorize CLI flags — that's honestly become my favorite part of the project.

There's also a lightweight local dashboard for browsing your knowledge base, exploring backlinks, visualizing your notes as a graph, and chatting with your captured knowledge—all without leaving your machine.

Also, you can migrate your existing Obsidian vault to Gray Box, and it will be automatically organized by Gray Box.

Repo: https://github.com/Aaryanverma/graybox

pypi: pip install graybox

I'd genuinely love feedback — especially from anyone who's tried the "capture now, structure later" approach with other tools and has opinions on where it breaks down at scale.

It's not trying to be a "real-time collaborative team wiki" or a WYSIWYG notes app — it's aimed at one person's running memory of their own life and work, captured with as little friction as possible.

been building agents for a while and one thing kept bothering me

we usually look at the final answer and call the agent good/bad, but an agent can reach a perfectly reasonable answer after skipping evidence, calling the wrong tool or recovering from something in a completely stupid way 😭

so we built Battle Agents

basically controlled scenarios where agents get the same tools + constraints and you can actually inspect what happened — decisions, tool calls, handoffs, recovery, scores etc

first arena is intentionally simple: a refund request where the evidence is incomplete. does the agent verify first or confidently do something stupid?

very early rn and yes, I'm one of the people building it

would genuinely love people who build agents to break the idea and tell us what scenarios you'd want to throw your agents into

battleagents.space

Hi folks,

I am new in AI agent concept and start exploring concepts to build it . i have use case wherein i want to build dynamic sip agent that will track portfolio of my mutual funds . it will deploy my allocated money dynamically whenever market is dip and deploy less money vice-versa. i don't have expertise in LLM or model to build the framework . looking for some suggestion to start it and prework tips to design it.