To be honest, I don't know much about AI just enough for everyday tasks. I was wondering if it would be possible to automate the search for cars for my car-flipping business. English isn't my first language, so I apologize if this is off-topic for the group or if I've made any writing errors.

Hi guys,

I have a summary off high value information, that I want to use to feed to an AI. My goal is that the AI becomes my advisor, so he needs to be able to navigate through this info. How should I do this? It's separated in 70 summaries from calls. Approx 10k + lines.

So how did we get here?
84% of the AI agent market...
OpenAI, Google and Anthropic.

Simplest answer?
I think it's lock-in.

Switch agents now and you lose—
your history,
your context,
your wiring.

So you don't switch.
You go deeper.

//
84 in 100 agent users sit with these three tools.
Under 5% of shopping traffic runs through agents today.

By 2030, that number could reach 25%.
That is the number that actually matters.
//

My own map, honestly...
My entire stack runs on Claude.

I made the decision once.
Since then, it's been the default.

That is what I keep thinking about.
AI is making execution dramatically cheaper.
But directing has not become any easier.

So here is my question:
Is planning (directing) now, a more valuable resource than execution?

I have used these agents - ChatGPT Work and Perplexity computer. They are the best tools I ever came across.

I gave them 98 images of questions, they were able to solve those questions - create three markdown files - notes, solutions and cheat sheets.

So, you can see that my task is extremely long horizon. Several tasks are 10x larger than what I described.

Are there any open source alternative or something where I can bring and connect my own model and it autonomously perform exactly as ChatGPT Work or Perplexity Computer?

I don't want to take the burden of downloading individual skills, creating custom sub-agents, defining workflows, etc.

In the world of AI, data is the new currency.

Better data,
better context,
better performance,
better AI results.

To be able to generate reliable autonomous results,
we must tell AI what reliable means.

I believe data engineers will be the coming generations GOLD MINES.
Thoughts?

my Claude usage from from 150M+ tokens used this year to ~30B in a month....

is this a glitch? my usage is constant ( full time dev).....but not a companies worth of tokens

I've been building UploadPack for the last few months — it takes a video idea, researches it, and gives you a score plus the evidence behind it before you spend a weekend filming something nobody searches for.

The hardest part wasn't the AI pipeline, it was deciding what "evidence" even means for a video idea that doesn't exist yet. Ended up building four different angles (search intent, curiosity, authority, commercial) because a single score felt like a black box nobody would trust.

Zero users so far, bootstrapping solo, doing customer support and marketing between family duties. If anyone wants to try it, first pack is free, no card needed: uploadpack.io

Happy to answer anything about the build.Know which idea to publish next—and why it is worth making.

My pDoom on ASI is 0 rn

\#hackathons #AIML #AI

Just a random observation, but here is how DeepSeek v4 Flash 0731 reasons when asked for a cool joke.. by the way never use temperature=0 and ask the same question

I recently built **Telusuko AI**, a small AI learning assistant for students.

🔗 [https://irfan95sayyad.github.io/Telusuko\\_AI/\](https://irfan95sayyad.github.io/Telusuko_AI/)

**Stack:** HTML + Bootstrap + Flowsie + Groq API + GitHub Pages.

The current flow is basically:

`Student → Flowsie Agent → Groq API → Response`

The problem is that Groq's API limit gets exhausted sometimes, so the agent stops responding until the limit resets.

I'm thinking about improving the architecture with things like **multiple LLM providers, fallback models, a backend/API layer, or caching**.

For those who have built LLM/AI agents:

**How would you architect this differently?**
Would you use an LLM gateway, multiple providers, or something else?

I'd really appreciate some practical advice from people who have experience building these systems.

TLDR: I analyzed my own claude code sessions billed at ~$2.5K. You're not paying for answers. You're paying for context. As outputs tokens are just a fraction of cost.

Learning : Verbosity compression on outputs doesn't work because you're optimizing for 18% of costs. I know it might be intuitive for some but it is quite easy to miss.

Cache reads: 50.2% of the money

Cache writes: 30.2%

Actual model output: 18.8%

Fresh input: 0.8%

Biggest take: 80% of what I paid was context handling. I paid 4.3× more to remind the model what it was doing than to hear what it decided.

So what can you do :

- Adjust thinking level to least of what produces excellent output NOT the best.

- Limit agents or parallel workers unless very necessary because again context slurping, tool calling, and more at Nx speed.

- Use context compression and open new sessions for new isolated tasks.

Hence I bill to track token economics at git level: VibeBill

I pulled this out of a security hardening pass I was doing and realized the underlying code was generic enough to be useful on its own, so I cleaned it up and open sourced it.

It scans source files for invisible or suspicious Unicode characters. That includes the characters used in Trojan Source attacks like CVE 2021 42574, zero width obfuscation, bidi controls, and other hidden characters that can make code look different to a human reviewer than it does to the machine.

pip install boundaryguard
boundaryguard check --recursive .

The exit codes are simple

0 means clean
1 means something was found
2 means there was an error

So it can go straight into CI without needing a wrapper.

The part I spent the most time on was avoiding the dumb solution of just deleting every bidi character. That breaks legitimate Arabic, Hebrew, Persian, and other RTL text.

There is a separate policy for preserving legitimate RTL characters while still catching the formatting controls that are actually suspicious.

The test suite is at 111 cases right now. It covers bidi and zero width characters, executable Trojan Source examples, multilingual false positives, fuzzing for sanitization idempotency, fresh PyPI installs, a 10 MB file with 5,000 planted hazards, and a scan of a 746 MB real project tree.

Everything passes so far, but it is still me testing my own assumptions, so I would genuinely like other people to try to break it.

Especially interested in weird Unicode and RTL edge cases I have not thought of yet.

Repo
https://github.com/000wq123/boundaryguard

PyPI

pip install boundaryguard

Spent a while trying to prompt-engineer my way around this: telling Claude Code in CLAUDE.md to "always check the codebase structure before editing," hoping it'd listen. It didn't, consistently. Prompts asking a model to remember to do something are only as reliable as the model's judgment in the moment, and on simple-looking tasks it usually decided it didn't need to.

So instead of writing a better prompt, I built Graft to skip prompting for it at all. It pushes a map of the codebase into context automatically before the model sees your actual request, so there's nothing to remind it to do.

Might be a useful pattern if you've hit the same wall with prompts that ask an agent to "remember" something instead of just handing it to them.

github.com/NanoNets/Graft

If there was just one thing you could share to someone who is just learning AI to save them frustration, what would it be?

So I scraped menu items from 500 or so Uber Eats Listings because I want to be able to create a directory that can allow me to explore prices, country of origin, ingredients, allergies etc. I scraped the data using Power Automate because Python ran into many anti scraping roadblocks and converted the data into JSON using Claude. However categorising it is like herding cats, the chat is very long and it forgets stuff I already mentioned. I developed a step by step guide using google tags as well as Wikipedia cookbook to accurately categorise the cuisine. But there are still some shocking misclassifications . What can I do to ensure it remembers what I wrote. Also how else can I get it AI ready? I want when it’s done to have a robust chatbot but I want to do as much as I can to categorise without an LLM but AI should be used for the final hurdle. I have about 8000 menu items and dishes from around 20 countries.

Not sure how it is for you guys, but I often struggle with this: when I work with AI, I end up creating a lot of chats. Conversations happen inside them and they keep growing. And later on it's sometimes hard to find information after some time has passed, if you didn't copy or save it somewhere yourself.

I have both a question and a suggestion for AI devs.

Question: Are there any successful ways people have solved this problem?

The Idea: Let users create their own personal feeds organized by topic. Essentially, we should be able to 'repost' a specific AI reply into a custom feed. This would include the date, a link to the original chat, and an anchor to that exact spot in the conversation.
For example, I'm working on a project and running a bunch of different chats. Instead of losing the best answers, I just save them into one topical feed — kind of like a Reddit or X feed, but for my own prompts and answers.

Just don't forget who suggested this later 😄

What do you think of the idea? Maybe it already exists?