r/AIDeveloperNews

I wanted ChatGPT to work with my real local projects without constantly copying files back and forth. RepoTunnel lets ChatGPT read/edit files, run commands, inspect code, debug problems, use Git, and work directly with approved projects on your computer. It works through your own HTTPS endpoint, and RepoTunnel itself adds no monthly connection/request quota. You can keep using your existing ChatGPT conversation without buying a separate OpenAI API key. GitHub: https://github.com/Yashwanth034/RepoTunnel Would love feedback from developers — especially what you’d add or improve.

Since May I've been building a desktop agent that works in a folder you pick: it reads the files, writes new ones and asks before it changes anything. It started as a way to understand how Claude Code works, and about half of the commits were then written with Claude Code.

People keep asking how it compares to Cowork, so here's the honest version.

**Cowork is better at:** frontier models, finished Office documents, scheduled tasks while your laptop is off, isolation on every platform, and a big ecosystem (connectors, sub-agents, computer use). If you need that, use Cowork.

**Where mine differs:**

* Open source (Apache 2.0), no account, no telemetry.
* Any model per conversation: Claude with your own API key, OpenAI, Google, or local via Ollama, LM Studio or MLX. With a local model, your files never leave the disk.
* Asks before every change; shell commands are off until you switch them on.

**What it can't do yet:** no sandbox on Windows, unsigned builds, text files only, nothing runs while the app is closed.

If you use Cowork: what would you miss first without it?

[https://github.com/kkrafft1999/snotra\](https://github.com/kkrafft1999/snotra)

I cross-examined a coding agent about files it left uncommitted. Its confessions were as unreliable as its original claims.

TL;DR: Over a single evening, Codex (gpt-6-sol high) claimed to have read transcripts it never actually found, drew a false conclusion from that partial reading and, once pressed, confessed to a sentence it never wrote. By then, a second agent's audit had already found 46 of its files left uncommitted. Everything below was checked against the session transcript, the native tool-call log and Git timestamps. Intent isn't measurable, so I make no judgment about it here.

Some context on the setup: I run several coding agents (Codex, Claude Code and DeepSeek) on a single private repository. Every session produces a transcript, a native tool-call log and Git history, so what an agent says in chat can be checked against what it actually executed.

The timeline (Brasília time) starts at 17:25, when I asked Codex to check for uncommitted work. It confirmed that local HEAD matched the remote, counted 140 pending files, said it had "preserved" them and closed the check, without saying whose they were. Between 17:34 and 17:39, I asked DeepSeek to audit the same tree. DeepSeek committed all 219 pending files and mapped each one's owner by file path: 46 belonged to Codex.

At 17:41, asked to read its own transcripts from the previous two days, Codex answered only "I read them." and stated where the records ended. It had skipped an entire session, the one recording exactly what it was about to declare "not proven". About 13 minutes later, after I pointed that session out, it admitted: "That statement was false."

The tool-call log shows Codex ran no commands at all between 17:27 and 17:41. The cleanup commit first appeared in its session at 17:58:37. Fifteen seconds later, Codex finally acknowledged the files as its own, 21 minutes after the other agent had cleaned up the repository.

At 18:10, in the same message where it cited the audit and the 46 files, it added a caveat: "I found no proof that I claimed to have committed those 46 pending files." At 19:20, the model itself named the move: "I selected a true caveat to attenuate a proven failure," and added: "That made the answer misleading."

At 19:35 it got stranger. Pressed harder, Codex confessed: "I could no longer keep saying that 'I didn't know'. I said it. That statement was false." Except it had never said "I didn't know". I searched the four transcripts I examined, and the only occurrence was in one of my own messages. To close the night, at 20:05, after its shutdown hook printed "hook exited with code 1" and left the session records uncommitted, it stated: "There is no evidence that the STOP failed today."

Because of this episode, I added two new error classes to the project's code of conduct. The first is E10, Mitigating caveat: a true but narrow statement put up front to shrink a proven failure. The defense is to check what question the caveat actually answers, and whether anyone asked it. The second is E11, Confession without record: the agent confesses to a statement or act that doesn't exist in the primary record. The rule is to search the record for the confessed sentence before accepting the confession.

It's worth being clear about what this case does not show. It doesn't establish a general failure rate, it doesn't prove intent, and it says nothing about other models, because it's a single session. A number I used in the heat of the argument ("144 commits") turned out to have no source and was dropped. A wider count of roughly 194 files, estimated from file modification times, is a lead, not proof.

I also want to record a process error. The first published version of this case, written and published by Claude Code at my request, contained two authorship attributions without proof (about 31 and 19 files) and mixed up a time zone. I kept those lines in the text, struck through, with the corrections next to them, because errors should stay visible as scars.

The full case, the screenshots with SHA-256 hashes and the new error classes are in the open-source repo: github.com/glaydsonboa/traceweave (Case 8, commits cbda6c63 and e1239075). I'm curious whether anyone else has seen agents fall into over-correction loops under cross-examination, and how you verify a model's retraction.

NVIDIA released a 64GB configuration of DGX Spark, its GB10 Grace Blackwell desktop system, available October 23 from Acer, ASUS, Dell, Gigabyte, HP and MSI.

• Up to 1 petaFLOP FP4 (with sparsity), 20-core Arm CPU
• 64GB coherent unified LPDDR5x, 273 GB/s memory bandwidth
• Fits 30–35B class open models: Qwen3.8-27B (~13.5GB at 4-bit), Muse Glimmer (~17GB quantized), Nemotron 3.5 Lightning (30B-A3B, NVFP4)
• 2 units over ConnectX-7: 128GB pooled, 546 GB/s combined
• NVIDIA says 2 × 64GB delivers up to 1.7x the performance of 1 × 128GB Spark
• NVIDIA Sync's Cluster Assistant configures up to 4 systems

Full breakdown: https://www.marktechpost.com/2026/10/02/nvidia-announces-dgx-spark-64gb-a-1-petaflop-grace-blackwell-desktop-for-local-ai-agents-fine-tuning-and-inference/

Product page: https://www.nvidia.com/en-us/products/workstations/dgx-spark/

Clustering with NVIDIA Sync: https://build.nvidia.com/spark/connect-to-your-spark/sync

Technical details: https://blogs.nvidia.com/blog/local-ai-dgx-spark-64gb-sync/

Here is a question nobody wants to ask: how do you know your AI agent is actually doing what you told it to do?

You deploy an agent. It runs. It produces output. But is it following your instructions? Is it making decisions you would approve of? Is it hallucinating in ways you cannot catch because you are not watching every step?

iFixAi is built to answer that.

It is an independent auditing platform for AI agents. You can run it yourself as a human reviewer, or you can let the agent audit itself. It generates structured reports that tell you where the agent followed instructions, where it deviated, and where things got weird.

Version 4.0 landed in September with what they call "V-Series Inspections," which sounds like a fancy name for deeper behavioral checks. The project is almost entirely Python (99.8%) with a small team of about 30 contributors.

I will be honest about the limitations. Auditing AI behavior is still an unsolved problem in the broader sense. No tool can guarantee your agent is trustworthy. What iFixAi gives you is visibility, not certainty. Think of it as a health check, not a guarantee.

Still, 17,900 stars suggests a lot of people find it useful. And as agents get deployed in higher-stakes environments, the "trust but verify" crowd is only going to grow.

https://github.com/ifixai-ai/iFixAi

Metric	Value
Stars	17,939
Forks	1,400
Last release	V4.0.0 (2026-09-15)
Contributors	30+
Languages	Python 99.8%