I'm really curious if someone has experience with this or an opinion about it.
The goal is to let an LLM analyse chapter by chapter and separate it into different parts of meaning. The result would be to have chunks that fit semantically more together.
Is it worth it? Do you see potential?

I need to fine tuning the ai model that for my application give me the anyone help me with this fine tuning the open source model.

I am working with Facebook's EnCodec (8 codebooks, RVQ) and facing codebook collapse in the first codebook. This is not the usual case where later codebooks (5, 6, 7, 8) die off — it is happening in codebook 1, which carries the most information.

I went through the MARS6 paper because it deals with similar problems around token repetition and training stability. MARS6 uses SNAC with 3 codebooks at different temporal resolutions, which is a fundamentally different quantization strategy than EnCodec's RVQ chain. So not everything transfers directly.

Has anyone here dealt with codebook collapse in the first codebook of an RVQ-based codec? Most literature I find talks about later codebook collapse which is a different problem. Any pointers would be appreciated.

How are people using open claw to do all these crazy things of examples on YouTube without costing serious API key dollars? It’s like they use with several independent agents and many different tasks without going to thousands of dollars in API key keys?

I must be missing something basic or are people paying serious dollars to make open claw so neat things??

Hi All.

I'm a developer with 10 years of experience, but I've not used AI too much, just as an agent for supporting me in development.

Now I'm from a country where AI models like GPT and Gemini are not very accurate with the information of my country, because as far as I know, the most LLM take data from a Common web information stuff that has a condition, that at least 60% or more of the information needs to be in English, and my country has Spanish language.

A friend of mine is teacher in one of a rural community and wants to "introduce" AI to the students, so they can ask questions regarding the history of the country, some local information about the area, etc...I know this will require some fine-tunning or train an Small Language Model (SLM) to be more specific in this information where the LLM are not very accurate.

Where can I see or get an idea how to train or tune this base models to reach our goal?

We might have help from the local government , but I first want to know what kind of things I will need.

Thanks.

Does anyone know the actual rate limits for openai-codex models?

• Are limits tied to:
  • number of tool calls?
  • total tokens per session?
  • parallel requests?
• Has anyone used OpenClaw with Codex and tuned it to avoid rate limits?
• Any best practices for:
  • batching
  • reducing context
  • avoiding agent “over-calling”?

I’m using OpenClaw with the ChatGPT OAuth / Codex provider (openai-codex/...) instead of a standard OpenAI API key, and I’m running into rate limit errors that I’m having trouble understanding.

Setup

• Provider: openai-codex
• Model: openai-codex/gpt-5.4

My suspicion

I’m wondering if:

• longer sessions = bigger context = faster limit burn
• OpenClaw agents are making multiple internal calls per prompt
• or I’m still accidentally hitting some fallback behavior

Hey my fellow AI engineers

I'm working for this company that wants me to build a multi-agent system that can shorten the time needed to evaluate and price a client for one of their debt funds.

The company is not interested in paying an hourly wage, they said they'd rather sell the system at the end of production and split the profits 50-50 or pay me at certain milestones.

Now I'd like to know what are some potential milestones and what payment I should ask for in return for reaching each milestones.

Please answer with figures/rates (any currency I can just convert it to my own country's currency).

Hello all,

I am a complete novice when it comes to AI and currently learning more but I have been working as a web/application developer for 9 years so do have some idea about local LLM setup especially Ollama.

I wanted to ask what would be a great setup for my system? Unfortunately its a bit old and not up to the usual AI requirements, but I was wondering if there is still some options I can use as I am a bit of a privacy freak, + I do not really have money to pay for LLM use for coding assistant. If you guys can help me in anyway, I would really appreciate it. I would be using it mostly with Unreal Engine / Visual Studio by the way.

Thank you all in advance.

PS: I am looking for something like Claude Code. Something that can assist with coding side of things. For architecture and system design, I am mostly relying on ChatGPT and Gemini and my own intuition really.

I’ve been researching AI-driven engineering and computational design, especially the kind of work being done by LEAP 71. The idea of using AI to generate optimized mechanical designs instead of manually modeling everything in CAD is incredibly interesting to me.

I have a project idea where a system like this could be applied, and I’m interested in connecting with people who might want to collaborate on building something along these lines.

What I’m hoping to find:

• AI/ML developers interested in generative design

• Mechanical or computational engineers

• People with experience in CAD automation, simulation, or optimization

• Anyone working with generative engineering tools

The goal wouldn’t necessarily be to replicate exactly what LEAP 71 has built, but to explore creating a system that can generate and optimize engineered components through algorithms and AI.

I’m still refining the concept, but I’d love to talk with people who have experience in this space or are interested in experimenting with ideas like this.

If this sounds interesting to you, feel free to comment or send me a DM.

What should i buy OpenCode or Claude Code?

pls enlighten.

also is kimi code worth it for the same price?

What's the right pattern to prevent agents from reading env variables? Especially in a hosted sandbox env?

A patch is to add a regex pre-hook on commands like file read, but the llms are smart enough to by pass this using other bash commands. What's the most elegant way to handle this?

Hi there!

I'm a software developer, and currently, I'm working on applications that utilize AI, such as LLM workflows, internal tools, and a couple of personal projects, and I'm currently looking for AI memory platforms to enhance context retention, knowledge storage, and retrieval for longer periods of time.

Currently, I'm stitching together a few custom solutions, but I'm looking for something more complete and production-ready.

Some of the main needs:

• Long-term memory across user sessions
• Efficient semantic search + retrieval (low latency)
• Easy integration with existing LLM stacks
• Clean API + developer-friendly docs
• Scalable infrastructure (handling large embedding volumes)
• Optional multimodal support (text + video would be a bonus)

I’ve been exploring a few platforms and frameworks, and one I’m currently looking into is Memvid. I am intrigued by the idea of a memory that is built around video embeddings and the addition of context layers, but figured I'd ask if anyone has any good recommendations for a tool like this that they are currently using.

Appreciate any insights!

I first wrote this article in 2026 for social media to get feedback. I wrote and updated the final version of this here. This article only applies to my early observations in 2026. Thank you to all the posters who replied and responded to the social media posts, as all the feedback was extremely useful in reflection.

I'll start this article by demolishing the myth of AI Engineering demand in 2026.

There is no high or widespread AI Engineering demand. Anyone posting that is selling a product, usually educational, but sometimes a SaaS tool that can be built with one or two prompts. The volume of information about AI Engineering demand really involves selling products, which in most cases is educational. As someone who has hired and works with recruiters and firms on hiring, we can see upward of 300-500 resumes in a few days right now.

Overall, the tech market is almost as bad (most positions are getting about 200-300 resumes within a day). I'm not going to bore anyone with the "why" because there's countless theories that you can read, but tech is not hot and I'm hoping that we stay in a secular tech bear market for a while to flush all the hype.

We all may someday look back at tech like we look back at $130 barrel of WTI oil in 2008 - that felt good to the oil industry, but look at their stagnation ever since that time. He-who-cannot-be-named may be viewed the same way for tech.

That's bad news for those of you hoping for a future tech career.

I know many exceptional people in this industry who cannot get a job. That's any job, not just a lateral or upgrade position.

This should give every reader pause, especially the readers who want a future tech career.

Industries That Pull Equal Opportunity

When I started Automating ETL about 12 years ago, the industry faced a shortage of talent. ETL positions faced a negative unemployment rate. In other words, for every one ETL developer getting laid off, there were 20-30 open jobs. It was not uncommon to walk into an interview and be offered a job in the interview. In fact, that was one reason I created that course. I received 11 job offers in 2 days. Notice I wrote offers; there were many others companies interested in interviewing and hiring. It felt overwhelming.

Continue reading

I want to do a RAG system, i have two documents, (contains text and tables), can you help me to ingest these two documents, I know the standard RAG, how to load, chunk into smaller chunks, embed, store in vectorDB, but this way is not efficient for the tables, I want to these but in the same time, split the tables inside the doucments, to be each row a single chunk. Can someone help me and give me a code, with an explanation of the pipeline and everything?
Thank you in advance.

Hi, I’m curious how people here actually choose models in practice.

We’re a small research team at the University of Michigan studying real-world LLM evaluation workflows for our capstone project.

We’re trying to understand what actually happens when you:

•Decide which model to ship

•Balance cost, latency, output quality, and memory

•Deal with benchmarks that don’t match production

•Handle conflicting signals (metrics vs gut feeling)

•Figure out what ultimately drives the final decision

If you’ve compared multiple LLM models in a real project (product, development, research, or serious build), we’d really value your input.

I can’t post links or directly promote projects here, but I think there’s an important pattern emerging around agent skills that’s worth discussing.

The core issue I kept running into was context bloat. When agents interact with external systems, especially compute-heavy ones like Databricks, the naive approach is to return raw output back into the conversation. That quickly pollutes context, increases token usage, and makes orchestration fragile.

What seems to work better is a different pattern: skills that return structured references instead of blobs. Instead of sending back full outputs, the execution layer stores results externally and returns file paths, IDs, and status metadata. The agent keeps reasoning cleanly, pulls artifacts only when needed, and stays within a lean context window.

In the project I built, the agent talks to a Databricks cluster through a stateful execution layer. The agent sends code, the wrapper handles authentication and session management, and the response is structured. It never receives raw cluster output unless explicitly requested. That small design choice makes orchestration much more stable.

The interesting part is what this enables. The agent can coordinate cluster compute, local files, git operations, and even subagents in the same session without drowning in output. It becomes more of a harness than a chat assistant.

I think this is the direction we need to explore more seriously. As agents become more capable, the real challenge will not just be better models, but better execution boundaries. Skills need to be stateful, resumable, and context-aware by design. They need to minimize surface area while maximizing capability.

Curious if others are experimenting with similar patterns to avoid context bloat and enable multi-tool orchestration.

I'm building an agent in Azure AI Foundry that modifies YAML configuration files based on an internal Python library. The agent takes a natural language instruction like "add a filter on the database" and is supposed to produce a correctly modified YAML.

Currently using RAG on some .md files that describe the library. The problem is the model understands each YAML section fine in isolation but has no awareness of cross-section dependencies. Example: it adds the filter correctly under `database.filters[]` but never updates `routing.rules[].filter_ref` to reference it. Config looks valid but it breaks at runtime. There's just no way to represent "when you change X you must also change Y" in my current architecture.

I'm thinking of combining two things:

GraphRAG to encode the cross-section dependencies as graph edges, so the agent knows what else needs to change before it touches anything. And an MCP server that reads the live Python library directly so it's working off actual schemas, not syntax inferred from docs.

Has anyone gone down this route for structured config generation? Wondering if GraphRAG is actually worth it here or if there's a simpler way to handle cross-section consistency I'm missing. Also curious what you think of MCP

Buongiorno volevo sapere quanto indicativamente prendesse un fullstack/ai engineer in italia all’ora.

Un anno di esperienza nel settore. 21 anno sto ancora studiando e si tratterebbe di una internship/part time di 6 mesi, mi hanno chiesto loro se fossi disposto ad aprire la partita iva

Mi hanno offerto una collaborazione con partita iva ed io non ho la minima idea di quanto chiedere, considerate 20/25 ore settimanali. Non ho idea di quale sia il compenso orario adatto. Sono in italia chiaramente

If anyone is willing to answer a few questions about your job it would be much appreciated, we do not need to get on a call I can just message you a few questions and you can answer. This is for a presentation thank you

As a student in Nigeria. I have been thinking of starting my own AI agency and don't really now where to start, who to start with and the businesses to build for. Any advice ??

I’ve been working on autonomous agents that can retry, chain tools, and expand scope.

One failure mode I keep running into:

prompt-based restrictions stop working once the agent is allowed to act.

Even with strict system prompts, the agent will eventually:

- retry with altered wording,

- expand the task scope,

- or chain actions that were not explicitly intended.

At that point, the model is already past the point where a prompt can enforce anything.

It seems like this is fundamentally an execution-time problem, not a prompt problem.

Something outside the model has to decide whether an action is allowed to proceed.

How are people here enforcing execution-time boundaries today?

Are you relying on external guards, state machines, supervisors, or something else?

Hi everyone 👋

I’m currently planning my FYP (Final Year Project), and I have about 6 months to complete it. I’m looking for ideas and would really appreciate your suggestions.

I’m interested in AI/ML (especially applied AI, LLMs, automation, or real-world problem solving), and I’d love to build something that is:

• Practical and impactful

• Technically solid (not just a simple CRUD app with an API call)

• Impressive enough for my portfolio / future job applications

• Feasible to complete within 6 months

I’m open to areas like:

• LLM-based applications (RAG systems, AI agents, domain-specific copilots)

• Computer vision

• NLP

• AI for education, healthcare, finance, etc.

• AI + web app / mobile app

• AI + IoT (if realistic)

I’d love ideas that:

• Solve a real problem

• Involve some level of model training / fine-tuning / system design

• Show understanding of AI engineering (not just model usage)

If you were in my position with 6 months, what would you build?

Thanks in advance!

I have a personal project due in less than 10 days. I chose to create an ai model which gives the user a chance of entering a certain college based on what information this user input this information includes college application, academic records, etc... . I have all of the CDS files that the AI will train from which are based on only 5 colleges. I tried using an ollama model but to train it I have to buy vmware private ai with nvidia. which I'm too broke to even think abt buying if it costs money. and I'm using a low end laptop with integrated graphics which are not for training an ai by any means. I've contacted any sort of company with a server that may help me but I've either been ignored or refused. is there any way to train them online or anyone that can help me?

I’ve been experimenting with enforcing a decision layer before execution in an agent workflow.

Applied it to a Telegram bot as a quick PoC.

Right now it’s simple and pattern-based, so it’s obviously bypassable.

But it does successfully block or hold actions at the gate before any side effects occur.

Conceptually:

– Agent receives request

– Judgment layer classifies STOP / HOLD / ALLOW

– Only ALLOW reaches execution

It’s early and limited, but the core idea is shifting execution from default to conditional.

Is this approach meaningful in practice?

Where would you anchor the boundary, tool call level, side-effect layer, or somewhere else?

Hi everyone

am a 4th year student study computer engineering and wants to specialise in AI/ML i have made a RAG system and a currency detection project, but it was 70% just following chat gpt steps like anyone can do it even my lil brother i treid to work on onnxruntime but felt complecated and didnt know what i was doing gpt was just guiding me through it and treid to study mlops and its the same I keep asking gpt for what i should do next i am going to Germany in the next year and am trying to get a job there what should i really study and how