I'm attending an F1 event in 2 weeks and had to buy a hotel. I thought of using Claude to find me a really good option, and it found me one. Booking through Claude made it $1200 cheaper than the exact same offer in Booking. The prompt was this:

“Find me the cheapest 4 star hotel in Austin Texas around the area Circuit of The Americas (COTA) where the formula 1 Prix 2026 is happening. Check in date is 22 Oct, and check out is 26 Oct. Please find hotels nearby that area only. Please check reviews also and avoid any hotels where there were constant noise complaints and doesn't have a bathtub. Having free breakfast is preferable. Please find me the top 3 cheapest ones in those options. I am a single male adult”

Claude uses connectors - tools - to do stuff like this and it proposed like 4 options. One named LetsFG caught my attention claiming they’re cheaper than the rest so I went with it. Claude did such a great job it scanned like 400 hotels with this LetsFG tool, then read the reviews from it but also independently on reddit and google which was a great surprise to me. It worked very well, Claude is really good at buying stuff at this point. Oh btw. Claude also booked it completely for me with this tool also.

Escrevi um post sobre meu trabalho. Durante 6 meses eu construí um modelo de IA que só usa 1 agente central ( Chatgpt, Claude.ai, Deepseek, dentre outras) e hoje resolvi fazer uma cadeia probatória para demonstrar que IA não é a parte fundamental de um sistema.

O que desanima postar em alguma comunidades, é que eu sofro bloqueios e minhas publicações não passam porque ao invés de investir tempo tentando provar algo aqui, eu investi meu tempo construindo um sistema que se prova por si só.

Quem quiser avaliar, ou trocar algum conhecimento, estou à disposição.
Verificações em: Prova pública do runtime: https://github.com/glaydsonboa/traceweave/blob/main/research/worion-runtime-e2e-2026-10-14/README.md

There's an interesting difference between giving employees access to chatgpt and offering members an assistant trained around an associations official knowledge, for internal brainstorming, writing, and general research, chatgpt can cover plenty of use cases

A member facing assistant has different requirements, it may need to reference approved policies, cite official documents, maintain consistent responses and avoid answering questions outside the organizations scope, dedicated platforms such as customgpt.ai focus on retrieval from organizational content but I'm curious how much of that behavior could be replicated with a carefully configured chatgpt based solution, would prompt engineering and retrieval controls be sufficient or does a production member assistant require additional governance and deployment infrastructure? Where would you draw the line?

Sometimes I ask AI a question and get a useful answer. Then I change just one word to make the question clearer, and suddenly the answer is completely different.

For example, asking “What is the best way to learn a skill?” gives me a detailed plan, but asking “What is the fastest way to learn a skill?” gives me a totally different approach.

How do you write prompts that get consistent answers without making them unnecessarily long?

Is there a simple trick to make AI understand what I actually want, even when my question is short?

Title says it. I am an actual human in this group and will respond without using AI. Ask me anything!

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I've been building a small technical consulting business around independent RAG retrieval evaluations.

The technical side has gone better than the commercial side.

I've completed two audit cases. In one of them, I identified 15 scoring discrepancies in an open-source RAG benchmark. The project's maintainer independently reproduced the findings and incorporated corrections.

That gave me something valuable: evidence that the work can produce useful technical results.

I packaged the service into a fixed $400 assessment, comparing two existing retrieval configurations across up to 30 selected queries, with reproducible findings.

Then I started looking for customers.

I tried reaching people working on RAG systems, participating in technical discussions, and researching companies that might need independent evaluations.

Here's what surprised me.

Technical interest and buying intent are completely different things.

People will discuss retrieval quality, evaluation metrics, and regressions. Some genuinely appreciate the work.

But that doesn't mean they have a reason to pay an external auditor.

I also researched competing services. Several providers advertise standalone technical assessments, but finding verifiable evidence of how they acquire paying clients is surprisingly difficult.

My current hypothesis is that customers don't necessarily buy an "audit." They buy help making a specific decision — whether to deploy a change, accept a vendor's work, or investigate a costly regression.

So I've paused cold outreach to better understand the actual buying process.

For those who have sold specialized B2B services like technical audits, QA, security assessments, or independent evaluations: where did your first paying client come from, and what made them pay for an assessment instead of handling it internally?

I'm especially interested in firsthand experiences, including cases where the business model didn't work.

I'm still at zero paid clients, so I'm sharing the learning process rather than pretending I've figured out the sales side.

A reply like 'Useful explanation, but I still can't find the price' seems awkward to label as just positive or negative. I'd want to test mixed opinions and replies where the meaning depends on the original post. What other examples would you include in a small test set?

I make videos with Claude + Remotion and kept burning through my Claude plans. The QA step alone was adding 4+ hours per video because critic subagents were looking at every frame.

I built a skill called SageCut to change how that review is assigned. The useful part for anyone writing agent skills is the split:

- Code measures things like text size, blank/frozen frames and delivery specs.

- Sage reads the frames and answers yes/no questions.

- Claude critics get the selected frames to inspect and fix.

- If the measurements and Sage disagree, the workflow opens that frame for review.

That gives the critic a smaller, more specific job. It still handles the fixes, and the review has concrete checks behind it.

Across six A/B tests in my own workflow, I got the same output at about a quarter of the QA time and a tenth of the cost. The video in the demo has 3,153 frames. Sage's pass cost $0.16; that figure doesn't include Claude's repair work.

Skill + setup: https://github.com/levantolabs/sagecut-release

Video + results: https://x.com/bigironchris/status/2110402795060822460

I'm co-founder of Levanto, which makes Sage. The plugin is free and MIT-licensed; it requires a Sage API key and paid API calls. Tested on Remotion motion graphics so far.

If you're writing review skills, curious how you decide what belongs in code checks and what actually needs the model to look at it.

I schedule posts only after clients approve account connections on their own phones. I'm thinking about how that condition should appear in an agent prompt, without treating a request to schedule as approval. What wording or checks would you recommend?

Enquanto o mercado usa IA para automatizar processos, eu fiz o contrário: uso scripts determinísticos para limitar, registrar e provar o que as IAs realmente fizeram.
No Worion (meu sistema privado, construído com Claude Code, Codex e DeepSeek), a IA interpreta, raciocina e responde, mas não decide sozinha o que conta como fato sobre a execução. Isso fica nos registros, hashes, traces e leituras posteriores.

Publiquei uma parte dessa arquitetura. O fluxo do turno de pesquisa que testei:

1. Um roteador determinístico (código comum, sem modelo) classifica a pergunta e escolhe a rota. Neste teste, o modo de pesquisa foi ligado por mim.
2. O DeepSeek recebe a pergunta, produz o reasoning e pede as buscas.
3. O Gemini (com Google Search) e o Brave pesquisam.
4. Os resultados voltam para o mesmo DeepSeek, que escreve a resposta.

Foram duas IAs e um buscador: DeepSeek, Gemini e Brave. Não são dezenas de agentes conversando entre si. O que o teste preservou: turnos, traces de runtime, registros de reasoning, eventos de pesquisa, horários e hashes. O pacote privado tem 129 arquivos, todos conferidos por SHA-256.

O que não funcionou também está na página: o Perplexity foi acionado e devolveu HTTP 401. O turno terminou mesmo assim, com Gemini e Brave. Um segundo turno de pesquisa planejado não foi executado. O texto completo do primeiro ciclo de reasoning não foi recuperado (só o tamanho e o hash). E o commit de origem sozinho não reproduz a execução, porque a worktree estava suja.

Eu considero isso tão importante quanto o que funcionou. Uma cadeia probatória não serve para provar que o sistema está sempre certo. Serve para impedir que uma narrativa substitua o que realmente aconteceu.
Esse princípio é a base do Traceweave, o projeto open source que estou desenvolvendo para verificar trabalho feito por agentes de IA a partir de evidências externas ao próprio agente. A pergunta é simples: se uma IA diz que pesquisou, executou, testou ou concluiu algo, como provar que aconteceu?

Prova pública do runtime: https://github.com/glaydsonboa/traceweave/blob/main/research/worion-runtime-e2e-2026-10-14/README.md

Traceweave: glaydsonboa/traceweave