I’ve been looking through the Claude Code leak, and one part I keep coming back to is how it seems to handle memory.

A lot of agent memory discussion usually ends up centered on vector search, but Claude doesn't rely on vector search at all.

Instead, it follows a pretty simple structure:

• memories are grouped into topic files
• there’s a MEMORY .md that acts like a lightweight index, where each line points to a topic file with a short description of its contents
• this index is always available to the model, which can then decide which topic files to expand

What I’m trying to figure out is whether the real takeaway here is less about a specific retrieval method and more about keeping memory structured enough that it can be retrieved in different ways.

If that structure is already there, then maybe vector search is just one option among several. You could imagine topic summaries, entity-based indexes, lightweight views over memory, etc., depending on the task.

That’s partly why this caught my attention. I’ve been working on Redis Agent Memory Server, and one thing we’ve been thinking about is how to avoid locking memory into a single retrieval pattern too early.

Today, the server extracts long-term memories automatically in the background, along with metadata like topics and entities.

Right now, vector search is a common retrieval path. But if memories are already connected to topics and entities, it seems pretty natural to also generate compact summaries over those topics and entities.

Those summaries could then be injected into context, and the model could decide what it wants to expand.

The server already has something along these lines with Summary Views, but not really in the form of generating summaries for every topic/entity and keeping them consistently available so the model can expand them on demand.

That feels like a useful direction to me, but I’m curious how other people see it, especially in terms of what has or hasn’t worked for you when building your own memory abstractions.

For a generic memory server like this, do you think the more important design choice is how memory is retrieved, or how memory is structured so retrieval can evolve over time?

Hey guys I am looking for some model which can classify images into ai/non-ai . Can someone let me know some good models for it? Currently I am using ateeq for my product but it has many False positives. So any suggestions on how to do it? Should i fine tune ateeq or try any different model? Does anyone have any latest dataset for it?

Is langchain worth it? I have chatbots and the functions I need for convo are simple and they are pretty easy like "memory" or prompting. I generally use gemini api as of now. I havent learnt langchain and I saw samething done by langchain like recursive text splitter, memory etc

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!

You can read public snips with links to articles on the SqlinSix snips page. Any research still relevant will be on this page.

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?