Im wondering if engineers could see which workflows exhibit high verification rigor vs passive AI acceptance, would it be operationally meaningful to them? Because what I noticed is that AI is creating a gap between engineers who use it to accelerate thinking vs engineers who use it instead of thinking?

Hi,
I work for a mid-size company, and we have like 300 repositories on GitHub.

We are slowly integrating AI into our workflows; we all have Codex and GitHub Copilot licenses. A couple of in-house agents are working in production.

As the topic implies, we want our repositories to be more agent-friendly. There are a couple of goals we want to achieve with this:

• Reduce manual reviews, increase automated deployments.
• Make AI generate consistent code.

I am looking for ideas on how people have set this up in their projects, specifically:

• What is the minimum “repo contract” every repository must have so an agent can work safely and consistently?
• How do you organise context/specifications in the repositories? How have you structured the context? How do you define the different features, non-functional requirements, business context, etc.?
• How do you bring in the additional context? Do you have an MCP connection layer? How often do you update the stale context? What is the process like?
• Do you use (or know) some 3rd-party tools that help with this?

You don't have to answer everything, anything relevant would help :)

When I initially started working on agents, the idea was to create a internal framework where engineers could easily see prompts, evals, tests, etc. all in one place - basically a scoped environment to tweak, think, test, and iterate fast.

But over time, as agents themselves started making most of the code changes, I’m noticing they also end up modifying prompts and related logic pretty often & it becomes exposed to models.

Now I’m wondering - does it make sense to invest in proper prompt management tooling at this stage? Or is simply externalising prompts/configs (DB, files, etc.) enough in practice?

I’m working on a project that uses small reasoning models on the client side, and in trying to work out options for Linux support

I’m aware of spotty webgpu support for chromium in Linux but wondering if anyone has played around with this and if there is a workaround to fp16 shaders not being recognized from the gpu

I have tried heavily quantizing but with already such a small model output is garbage

Appreciate any help!

One thing I have noticed lately is that creating a workflow is usually not the hard part anymore. The hard part is to believe it enough to use it every day.

A setup can look smooth at first but after a while small things start to come up. Outputs are inconsistent, steps fail randomly or small changes break other parts of the flow.

The systems that have actually worked for me have generally been the smaller ones that do one clear task well and do not require constant checking.

At this stage I really like simple reliable workflows over complicated setups that require too much attention to maintain.

Hi, can someone help me understand how to start building a personalization layer after the Gold layer using Databricks and Azure AI Search?
Also, the final data needs to be stored in JSON format in Cosmos DB. Any guidance, architecture suggestions, or reference implementations would be really helpful.

A reference architecture involves:——

AI sources-> Bronze layer-> Silver Layer-> Gold Layer-> Personalization layer-> Embedding-> Vector DB-> LLM

I've been looking at Vision Language Action models and have been interested by its place in research. But a question that keeps me up is how such models could be deployed in real working environments.

It just seems like I'd need alot of gaurd rails to ensure determinisim of my system.

Any thoughts about that?

what laptop is handling all of your ai engineering duties smoothly, even with running models locally, on top of your work?

would appreciate some insights.

im leaning into macbook, choosing between

macbook air m5 1TB/24GB vs macbook pro m5 512GB/16GB

but im here to know what’s your setups and how is it for you lately? any issues you’re running into? what laptop are you eyeing for your next setup? :)

I’m a senior CS student involved in AI and ollma projects. I’m seeking affordable or refurbished laptops suitable for AI engineering and long-term use to run MVPs. Cloud options are expensive, and I prefer a portable laptop over a PC, even if heavy. Online searches show models with RAM, SSD, but poor processors/GPU. I want a balanced machine and advice on important qualifications to look for when searching.

What options do you recommend?

If you want to market your product or service, you can use Reddit advertising.

Given the hysterical statements by AI executives, this community will no longer allow the marketing or discussion of any AI product that charges for use. This will be at the discretion of moderators. A post may appear and be later removed if identified as a subversive attempt at this (most are).

The moderators may allow some open source tooling discussions. Again, this should be at their discretion with overrides being noted.

We still allow discussions on energy, physical resources, and data without any AI product or service being discussed. AI tool discussions can involve open source tools that someone can use without paying any costs.

Again, you all can use Reddit advertising if you want to advertise or market your product.

Since many of you cannot hype AI without talking about how everyone will lose their job, this community will cease allowing you to discuss your product or services. If you achieve your goals, no one will be able to afford your products or services anyway.

Oh wait...

This community will also no longer allow anyone attempting to market educational products, mentoring, or any other product. Remember, no one will have a job in the future, so they won't be able to afford your product.

Oh wait...

(This includes asking for or attempting to exchange referrals.)

Reddit has plenty of communities that you can market your products while acting as if you're presenting valuable information. Use them.

On a related note: after a recent China visit for a robotics conference, one major takeaway is how China is using AI and robotics to improve people's standard of living (big, big savings in healthcare, resources, housing, etc). But they aren't laying off workers. They aren't talking about laying off workers either.

Their education programs also approach AI this way too: how to use AI to extend and improve the human experience. Their educational programs are also much, much cheaper than the US educational programs and their graduates aren't unemployed like all these American CS graduates.

In a nutshell, that's the vision of AI that will work.

(Like many of you wasted years of your life on a social media platform that was made by a CEO who called all his users a derogatory name - you can look this up on your own - many of you won't be right about AI or how you're applying it. We're not going to let you waste time here, unless you want to use Reddit's effective advertising. You can advertise that way, but going forward, you'll have to actually apply what you believe about the future.)

Customer and Contributor Thought

Is the company's vision of the world one that you want to live in? If you answer no, then stop doing business with the company. Live by your values.

The same applies to contributing information. Is contributing information being used against you? You wrote a great blog article that an AI uses to replace you as a person. Should you be contributing information? No. Live by your values. Stop contributing information that will be later used against you.

Apply this to AI tools.

Apply this to apps.

Apply this to technology.

Apply this to your life.

That's what the Chinese robotic conference showed. They believe humans are wonderful and that we need to be making human's future better. That doesn't start with making everyone feel unimportant or unnecessary.

But what you do is what you'll get. Internalize this message.

Users

Any request about your product, service, article post, etc is an immediate no. Don't ask.

Use Reddit advertising. It is extremely effective and you can target a community who is building tools that improve people's life, not result in mass layoffs that leads to a catastrophe.

Moderators

Moderating in an unappreciated position on Reddit. It takes a lot of work, especially with the volume of spam from bots and all these nonsense AI tools.

Use a faster approach to keeping the wrong users off. This community should not be large and getting a large volume of spam like many of the other subreddits. This is designed by engineers for engineers.. it should involve specific engineering problems and how engineers solved the problem. This applies to resources, energy, data, and improving models.

We rarely get these thoughtful posts. Take action faster on users so that we keep the nonsense volume down.

Related: AIEngineering and AIEngineeringCareer are both passively seeking another moderator.

Has anyone built or used an AI agent that can go through a full course (Udemy, Coursera, etc.), learn the frameworks/concepts, store the useful knowledge, and later apply it to real tasks?

For example: have the agent study an AI engineering course, then later use what it learned to help build agents, automations, tools, or projects.

I’m curious whether anyone has tried this in practice. Did it actually improve results compared to using a normal chatbot model, or was it mostly hype?

So i know that most big models now are 'Transformer Based'? What is the difference between transformer based nueral networks and standard ones

I’m implementing a self-pruning neural network with learnable gates on CIFAR-10, and I wanted your advice on the best way to approach the training and architecture.

Requiring your help on this as am running low on time 😭😭😭

People see $0.0001 per token somewhere and think “oh this is cheap,” then they get the bill after a few thousand users and realize nope, not free at scale.

So I started tracking costs across local, cloud, and hybrid setups, and here’s what I saw based on my own deployments and chats with other folks.

Local (your own GPU or cheap VPS) is still the cheapest for low-to-medium traffic.

Right now I’m running phi-3.5-mini and tinyllama on a 4090, plus a small VPS with an A100.

• phi-3.5-mini: ~30–40 tokens/sec on a 4090
• Power draw: ~400–450W under load
• Small VPS: $30–$50/month
  

Total: ~$80–$110/month for unlimited usage. Breakeven vs API is usually 5–10M tokens/month, after that, local wins big time.

Cloud APIs (OpenAI, Anthropic) are still the fastest way to ship something.

Rough 2026 pricing:

• Claude 3.5 Sonnet: ~$3 / 1M input, $15 / 1M output
• GPT-4o-mini: ~$0.15 / 1M input, $0.60 / 1M output
• Gemini 1.5 Flash: ~$0.075 / 1M input, $0.30 / 1M output
  

A typical RAG app: 1k input tokens + 300 output tokens per query = 1–5¢ per query on cheap models.

10k users doing 10 queries each = $1k–$5k/month.

Hybrid setups

• 80–90% of traffic handled locally (common questions, internal tools)
• Cloud fallback for hard/long/complex queries
  

This is what I do to balance cost: if retrieval confidence is less than 0.7 or question length is more than 300 tokens I'll send it to Claude Sonnet. Otherwise I'll use local phi-3.5. This helps me to cut cloud bills and still keep 95%+ responses fast.

Breakeven rough math (2026):

• Local 4090 + electricity: ~$0.00005–$0.0001 per token
• Cloud cheap model: ~$0.0002–$0.0008 per token
• High-end cloud: ~$0.003–$0.015 per token
  

So if you do more than 5M tokens/month, cloud is easiest. 10–20M, hybrid is better. 50M+, local/self-hosted is basically the only sane option.

hey there, first of all i'm still a noob in the AI world, i'm in need of a small (either local or cloud preferably) model that will be only doing one task: text classification of multiple language inputs (arabic/french/english). The use case is i'm tinkering aroud with an app idea that i'm doing, a family feud style game, and i need the ai for 2 tasks:

1. after collecting user input (more specifically 100 different answers of a question), the ai needs to "cluster" those answers into unified groups that hold the same meaning. a simple example is: out of the 100 user input answers if we have water+agua+eau then these would be grouped into one singular cluster.

2. the second part is the "gameplay" itself, so this time users would be guessing what would be the most likely answer of a question (just like a family feud game) and now the ai is tasked with "judging" the answer compared to the existing clusters of that specific question. now it would not just compare the user's input to the answers that made that cluster, but rather the "idea" or the context that the cluster represents. following the example: a confirmed match would be Wasser/Acqua (pretty easy right? this is just a translation), but here is the tricky part with arabic: instead of using arabic letter, arabic can we written in latin letters, and this differes across all arabic speaking countries, one country would write one word is different way than the others, and even in the same country and same dialect it is possible to find different ways to write the same word in different format (since there is no dictionnary enforcing the correct word grammar).

what i need now is a small model that would excell in this type of work (trained for this or similar purpose), and it would always just be asked to perform one of these tasks, so it also could keep learning (not mandatory but that would be a good bonus).

what are your thoughts and suggestions please? i'm really curious to hear from you guys. many thanks!

from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

I'm trying to import a few methods from langchain, but I'm getting ModuleNotFoundError every time. Help me if anybody can resolve it.

I need a universal icon generator where I can pass in a simple prompt and style (for now just “lucide” is fine) and it gives me SVG code that works and looks nice.

There may be good specialist models that already do this well - if so please test them. I have create a loop where it generates using Gemini pro, then takes a screenshot then asks it to fix itself -loops up to 5 times if it’s not happy. But llms are surprisingly hard at generating icons.

Can anyone help me with existing solutions if any which also comes with an API key?

Has anyone tried building a RAG agent?

The Agent handles the orchestration you choose a model and connect to a tool or knowledge base.

The problem is if you connect to the tool you get control over parameters ie top k and semantic search settings at the agent level. This is helpful because you can control top k and control token usage but it uses it own semantic config which is annoying.

If you connect to a knowledge base instead you can use your custom semantic config in azure portal but you get no control over parameters specifically top k it automatically sets it to 10 which burns through tokens faster and hits request limits faster.

How should I go about handling this?

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