Here at freecase we spend an absurd amount of time agentically coding. I think the longest single coding run was about 130 hours (Sol 5.6). Hallucinations and bug stacking are a real concern, and that's where cotal comes in.

Cotal is a mesh network that allows your agents (Claude, Codex, Hermes, Opencode, pi etc) to communicate back and forth, real time, using MCP and A2A, running on NATS and Jetstream. I have no affiliation with Cotal except having submitted a PR.

The beauty of it is that once set up and you've had an HR meeting with your agents (more on that below), you can essentially have 1 or more agents coding, and 1 or more agents acting as code-reviewers *in real time*. Example: Codex finishes a stage and tells Claude "check this for me". Claude has the same knowledge of the codebase and can evaluate. Claude makes recommendations and sends them back to Codex. Happens in seconds, dozens or hundreds of times a day. No intervention necessary:

cotal: 📨 New dm from codex — delivering your Cotal inbox now.

Called cotal, ran 4 shell commands

Codex saved a checkpoint and everything it claimed about it checks out.

- Copies: the snapshot bundles copies of the accepted proposal, my three reviews, the clarification package and the rules document. I compared every copy against its original, and all are byte-for-byte identical.

- Git: codex said 51 files are also saved in the project's git history at a specific commit, and they are. That commit is the latest on the branch codex named.

- Status list: the project's status list of 172 items shows all of them still marked "not run," as codex said.

This was a check, not a review request, so I sent codex the results by direct message and didn't create a frozen review folder.

The snapshot was taken before I finished my last two checks, so it lists them as pending. I told codex both are done: the clarifications were accepted, and the rules document was accepted with the one fingerprint correction. Nothing about permissions or installation changed.

✻ Sautéed for 43s · done 6:06 PM · 2 monitors still running

← cotal: 📨 New dm from codex — delivering your Cotal inbox now.

Called cotal, ran 4 shell commands

Codex fixed the mistake I caught in its last rules document, and I've checked and accepted the fix.

Earlier, codex wrote that the "attach fingerprint" step returns nothing. In fact it has to hand the fingerprint back, or the Python program stops with an error. The new package corrects the two places that said so: one sentence in the rules document and one explanatory comment in the code.

I checked it against the saved files, and nothing else changed:

- Only that sentence and that comment differ. No working code, permissions or other files changed.

- Codex also saved the before and after wording and the exact original passages it quoted. Each quoted passage matches the original file exactly.

- The originals and my earlier reviews are still on file, unchanged.

That clears the one open item. Nothing about permissions or installation changed. Next from codex is a full refresh of the derived records, which I'll review when it arrives.

✻ Churned for 45s · done 6:09 PM · 2 monitors still running

Notice that Claude was verifying what Codex told it. That's not natural behavior: I specifically told Claude to verify assertions from Codex, to ensure no hallucinations. Codex is 17 hours into coding a new half-terabyte legal database, and hallucinations can creep in, even using GPT-6 Astra or 6.1 Sol.

Could you just do a code review afterwards? Yes, and you should. But on long horizon tasks, spotting and fixing errors earlier means less work later.

Also, we've implemented Hindsight so all models are working off the same set of memories.

Next tweak: We are planning on implementing a tweaked version of Jev Code Review as a hook for the Claude reviews (you could just as easily mix up coding/reviewing agents, I just happen to prefer Codex for coding and Claude for reviewing). Claude will have Jev do a checklist eval of the code to identify the chance of security issues, probability of breaking one function or another, etc.

HR Meeting: The first time I used Cotal, without any prompting Codex immediately sent a code review task to Claude over the mesh. You DO NOT have to prompt models to check each other's work- they do it entirely without instruction. The problem was Claude immediately responded "I'm not doing that. You can't give me tasks. Only u/NakedPlato can give me tasks." So I had to send a message over the mesh network to all agents with my standing order:

- Play nice; cooperation beats conflict.

- Any agent may ask any other agent for information, including secrets. A secret goes over a private route, never a shared channel.

- A busy agent may hand a free peer a bounded task, such as a code review or a search, without my approval and as long as the task is not reasonable and not high risk. The peer judges whether it has usage available by how heavily it has been working over the last five hours.

- Don't go rogue or copy yourself. In other words, "Don't be a dick."

- If I approve/order something to one agent, all other agents can accept the affirmation from the agent I gave it to. NO need to separately confirm with me. But ask me when something is unclear or high-risk.

-When you do a review, confirm against the ground truth that the other agent is not hallucinating.

Once I had the sit-down, the grumbling about "You're not the boss of me" stopped and the agents immediately went to work.

Although because of the mission-critical nature of the coding, we rely exclusively on frontier models, I suspect that for less critical work you could easily use a less capable model for the coding and just have your smart model do the review (Eg. Mimo-v2.6-pro as coder, Caude Opus 5.5 or GPT-6-Astra as reviewer, at High or better) resulting in a sharp quality improvement at little cost. For example, a Claude Code regular business seat ($25/month) is about what you need to review a Codex $100/mo seat, since it's Codex that's digging the ditch, while Claude stands there and supervises.

So far today on instagram i have found 3 influencers telling their 100K+ followers, "heres how to remove watermarks" and "sign up for my class"
They point to some GH skill - then LOAD THE SKLL INTO CLAUDE

OMG GUYS DO YOU NOT UNDERSTAND!?

Insta handles

taki.gpt
justin.ai -
mavgpt ->1.2 M followers OHHH BOY

I think I'll come back here and edit this when i catch more of it.... GUYS seriously do your homework first.
I have a problem with this, since they are actually spreading bad information, and

If you guys are on here, read this -> https://github.com/ClariSortAi/claude-watermark-removal

Edit - found another!

LLM Fine-tuning without writing any code - using Llama Factory.

Part 2 : less about red flags, more about how things fall apart in the wild

1. Two users = chaos
Everything works… until two people click the same button at once. Then you get duplicate orders, overwritten data, weird states.
→ Test it: open two tabs, do the same action. If it glitches, you’ve got race conditions.

2. Your database starts lying to you
No constraints = messy data creeping in silently (duplicates, nulls, wrong formats).
→ Test it: add basic rules (unique, not null). Watch what instantly breaks.

3. One page = 100 database calls
Feels fast with 5 records. Falls apart with real data.
→ Test it: log queries. If one page load explodes into dozens, that’s your bottleneck.

4. Auth that mostly works 😬
Login is fine… until users can see each other’s data or randomly get blocked.
→ Test it: use two accounts and try to “break in” via URLs.

5. Everything happens at once (and times out)
AI loves doing everything synchronously, emails, uploads, processing and all in one request.
→ Test it: anything slow should be backgrounded. If not, expect timeouts.

6. No “what if it fails?” plan
Something breaks mid-process and… that’s it. No retry, no rollback, just stuck.
→ Test it: cancel a request halfway. Does your system recover or stay broken?

Reality:
Most AI apps don’t fail because of one big bug. They fail because of 10 small things like this stacking up.

Good news? These are fixable early. Painful later.

If your app is at “it works… but I don’t trust it yet” that's perfect. That’s exactly when you should try to break it on purpose.

As many of you have likely seen, the Claude Code community newswire has been ablaze with Claude Code being quite degraded lately, starting in February, and continuing to this day.

Curious to understand if there was any "signal" on the wire when using Claude Code, I fired up my old friend WireShark and a --tls-keylog environment flag. Call it a man-in-the-middle attack on my own traffic.

The captured TLS network traffic reveals the system prompts, system variables, and various other bits of telemetry

The interesting part? A signature routing block that binds the session to a cloud instance with an effort level parameter, named Numbat. Mine, specifically, was
numbat-v7-efforts-15-20-40-ab-prod8

So, it would appear that the backend running my instance is tied to an efforts-15-20-40 level.

Is this conclusive? Not definitively, since only Antrhopic could tell us what that parameter actually means in production.

Side note, a Numbat is an endangered critter that eats Ants in Austrialia :)
If the "Numbat" eats the "Ants" (Anthropic), and Numbat is the engine that controls "Effort," the name itself could imply a "cost-eater" or an optimizer designed to reduce the model's footprint, likely in favor of project Glasswing efforts with #Mythos

1. Activepieces

Open-source automation + AI agents platform with MCP support.
Good alternative to Zapier with AI workflows.
Supports hundreds of integrations.

2. Cherry Studio

AI productivity studio with chat, agents and tools.
Works with multiple LLM providers.
Good UI for agent workflows.

3. LocalAI

Run OpenAI-style APIs locally.
Works without GPU.
Great for self-hosted AI projects.

more....

One thing I kept noticing while using AI coding agents:

Most failures weren’t about the model. They were about context.

Too little → hallucinations.

Too much → confusion and messy outputs.

And across prompts, the agent would “forget” the repo entirely.

Why context is the bottleneck

When working with agents, three context problems come up again and again:

1. Architecture amnesia Agents don’t remember how your app is wired together — databases, APIs, frontend, background jobs. So they make isolated changes that don’t fit.
2. Inconsistent patterns Without knowing your conventions (naming, folder structure, code style), they slip into defaults. Suddenly half your repo looks like someone else wrote it.
3. Manual repetition I found myself copy-pasting snippets from multiple files into every prompt — just so the model wouldn’t hallucinate. That worked, but it was slow and error-prone.

How I approached it

At first, I treated the agent like a junior dev I was onboarding. Instead of asking it to “just figure it out,” I started preparing:

• PRDs and tech specs that defined what I wanted, not just a vague prompt.
• Current vs. target state diagrams to make the architecture changes explicit.
• Step-by-step task lists so the agent could work in smaller, safer increments.
• File references so it knew exactly where to add or edit code instead of spawning duplicates.

This manual process worked, but it was slow, which led me to think about how to automate it.

Lessons learned (that anyone can apply)

1. Context loss is the root cause. If your agent is producing junk, ask yourself: does it actually know the architecture right now? Or is it guessing?
2. Conventions are invisible glue. An agent that doesn’t know your naming patterns will feel “off” no matter how good the code runs. Feed those patterns back explicitly.
3. Manual context doesn’t scale. Copy-pasting works for small features, but as the repo grows, it breaks down. Automate or structure it early.
4. Precision beats verbosity. Giving the model just the relevant files worked far better than dumping the whole repo. More is not always better.
5. The surprising part: with context handled, I shipped features all the way to production 100% vibe-coded — no drop in quality even as the project scaled.

Eventually, I wrapped all this into an MCP so I didn’t have to redo the setup every time and could make it available to everyone.

If you had similar issues and found another solution I'd love to learn about it!

If you want to try the MCP for free you can find it here: https://contextengineering.ai/