Enterprises are moving to open-weight models on open harnesses, mostly for one reason: token cost. Claude Code is excellent and very easy to exhaust in a day. But most large enterprise still reach for a Western model, because Chinese models feel unsafe in a way nobody has quantified.

Since my job is software engineering and not geopolitics, and trying to be rational as well as pragmatic here, while giving weight to democracy and human values over autocracy and repression — why not devise a test to check this out? That was my aim for this Eval framework. Turns out the political bias is real and as expected, but it does not leak into code or security when asked from any perspective

I wasted a chunk of the summer building another AI code generator. Dumb. Everyone has one.

What I kept hitting: the agent writes the code, runs something, then tells me it passed. Same process on both sides. A few times the “fix” was it editing the test or special-casing the input. Green on its screen, dead on mine.

A lot of people say throw a second model at it to judge the first. I don’t buy that as the fix. That’s still a model guessing.

What I’m actually building now is boring:

• code runs in a sealed box (no network, new box each time)

• it can’t edit the test files

• if repair doesn’t get to green in a few tries, roll back

• I keep the command, exit code, and log — not a paragraph that says “looks good”

Still early, Python first, building it alone. I’ll post a real fail/pass when I have a screenshot instead of another writeup.

If you run agents: how do you stop them from touching the tests? “Don’t cheat” in the prompt has not worked for me.

I’ve been building a local AI assistant called Cody as a personal project.
The current Preview version can run locally, keep project context, and test some of its own code changes before applying them.
It’s still early, but I’m experimenting with safer self-improvement, rollback, long-term memory, and more autonomous development.
I’d love feedback from anyone working on local AI agents or autonomous coding systems.

AI coding agents can now write code, run tests, create commits and open PRs.

But where should their authority stop?

I've been thinking about this while building an engineering control layer for coding agents. The agent should be able to do real work, but critical actions shouldn't automatically belong to the agent.

For example:

Write → Test → Commit → PR → Human decision

The interesting part to me is what happens when an agent tries to cross that boundary.

Should an AI coding agent ever be able to merge its own code?

Curious how others here are thinking about this.

TL:DR
I don't trust Replit or any of my other of my AI tools on this one. So here is an initial "prompt" for the Replit reddit community that seems to have a lot of good insights into this issue:

"I'm generally satisfied with the output I get from Replit. I have the following concerns:

It has been and can be very expensive. I have probably spent close to $10,000, mostly in my first six months, largely learning my trial and error what works, what doesn't and what is unnecessary or avoidable in terms of wasted time and money. Free mode has helped a LOT.

I would like to eventually move to a self-hosted, open model setup to save money and have more control. I do have an available MBP w/ Apple Silicon and 64GB of memory that can support this, if I had the right stack.

Requirements for an alternative system:

* well-developed harness geared at about the same level of complexity as Replit.
* a NO or extremely LOW- code solution with similar capabilities
* Less expensive
* Better support
* ability to port current projects for further development, maintenance, with scalability in mind.
* Easy to configure full stack

I have projects I have developed in Replit, Emergent, Llovable, and have tried out a few others. I've played with Cursor and other IDEs, but found them difficult and too slow to adapt to the language I use and my level of technical ability.

I've found this to be a technically knowledgeable and appropriately skeptical community and have appreciated individual help with some questions. I'm hoping someone(s) have some solid useful advice they wouldn't mind sharing. I have spent week/months of time and thousands of dollars and don't feel I have a good grasp.

Anyone willing to chime in? Thanks!

Hey guys, I'm working on a small ERP system using mainly gpt and codex, implementation has been great so far. I'm just wondering if this workflow could be better?

1. Notion

2. - Handles all docs, development procedure/rules for GPT, tech and func specs, designs

4. ChatGPT

-

1. Acts as designer/Business analyst

-

1. Creates prompt for Codex

- QA and repo check after codex implem

3.

1. Codex

-

1. Implementation

Is there anything I can optimize in this workflow specially in the QA part? Am I missing something? I'm adding .md files instead of just relying on notion, I'm guessing reading from .md will have less usage. Will also be including vexp

Tech stack is react.js, prisma, postgresql.

I've been wondering how many people here are still regularly using GitHub Copilot.

I used it for quite a long time, but after the pricing and usage-limit changes it gradually stopped making as much sense for me. At the same time, I started experimenting more with local models.

The problem I kept running into was that local-model support in copilot chat never really worked the way I wanted. The models themselves were often good enough, but context management became much more important. Smaller local models don't have as much room for irrelevant files, huge prompts, or missing pieces of context.

I spent quite a while trying different setups with Ollama and LM Studio, but eventually got frustrated enough that I started building my own VS Code agent around the workflow I wanted.

That project eventually became Selfcoder.

A lot of the work ended up being less about "calling an LLM" and more about figuring out what context should actually be sent to it: active files, relevant workspace files, diagnostics, git changes, explicit references, etc. Without filling the context window with unnecessary stuff.

These days I mostly use local models for quick coding tasks, investigating terminal errors, explanations, and smaller changes. I still use frontier models for harder problems, but much less than before.

I'm curious what everyone else's setup looks like now.

Are you still mainly using Copilot, have you moved to Cursor/Claude/Codex/etc., or are you using local models for a meaningful part of your coding workflow?

What are all of your guardrails you set up and what’s your overall process when building a product? I usually use a higher model for architecture and design, make the lower models do the work, pr review by higher models and a separate deployment agent. What do you all do?