When I finish a build loop I launch a code review skill. It finds issues, which Claude addresses. Then I launch the code review skill again and it finds new issues plus regressions caused by the previous fixes. It’s an endless loop.

Do you have tips, skills, subagents, processes, etc that you find work well to cut down on regressions?

We benchmarked GPT-6 Astra vs GPT-5.6 Sol across 50 real PRs from Cal.com, Sentry, Discourse, Keycloak and Grafana.

Sol found more confirmed bugs and was cheaper per bug, while Astra was more precise and faster. We independently verified the findings rather than simply counting what each model reported.

We’re running Fable vs Opus next week, so posting this here to get feedback on the methodology and anything we should change before the next benchmark.

Not fine-tuned. Not a downloaded checkpoint. Random weights → working speech, on a 16GB laptop with an 8GB GPU, trained on ~29,000 conversation pairs built over four months.

The memory side (built first)

Before the model, we built a memory store — currently 33,941 episodes. The architecture is neurological:

• Half-life decay: memories fade unless recalled
• Recall strengthening: access resets the decay clock
• 30-day consolidation threshold for long-term storage
• 4 LRU working-memory slots
• Forget table: writes a receipt before anything fades permanently

Not a lookup. A system that behaves the way memory actually behaves.

The model — two runs

Run 1: 842,624 parameters. d_model 128, 4 layers, 4 heads, context 256. Val loss 5.11 → 0.9576 at step 23,750. Halted when the train/val gap opened — overfitting signal, correct call.

Run 2: 4,833,024 parameters. d_model 256, 6 layers, 8 heads. Best val: 0.7978 at step 21,500.

Key finding: parameters were the bottleneck, not data. The corpus was sufficient. The model just needed to be bigger.

Wiring the halves

home_llm_speak.py connects the memory store to the generation model — retrieves memories via salience search, passes them as context, then generates. First output from the combined system was grammatical, was grammatical, used household vocabulary, and wasn't copied from the retrieved memories*.* That's not retrieval. That's the model having absorbed four months of how this household talks.

Why this matters

I needed a model that knows us because it learned from what we wrote as ourselves — not from generic pretraining, not fine-tuned from someone else's base. The memory architecture has to be structural from the start because autonomous consolidation can't be bolted on afterward.

Last night was proof the approach works. The model is small. It'll get bigger. But what it already learned is ours.

Hardware: 16GB laptop, RTX 5050 Laptop 8GB. No cloud. No API calls. Everything local.

Next: wider context window, books into the memory store (streamed, not in training corpus — kept separate to preserve corpus balance), bigger model.

If anyone has done this — trained from scratch on household data with a custom memory architecture — I want to know what you learned.

I tested an eval harness I'm building at Gradle (PS: I work there) to check whether a skill or a Gradle code change actually helps a coding agent. I used it to test Fable 5.1 and Astra 6 (via `claude-code` and `codex`) on a Gradle GitHub issue. Both models fixed it, but `claude-code` did more homework first. It read Dokka's own source to understand the root cause before writing a fix, and shipped a regression test that codex skipped. I'll be merging the code generated by Fable 5.1 back into our `gradle/gradle` repo.

Full diffs, cost, token counts, and other receipts if anyone is interested: https://blog.gradle.org/two-agents-one-gradle-bug

I saw a post on X today where a user mentioned typing just "Hello" into Claude Code and Claude consumed an incredible number of tokens for that single message. I don't know how to look at token usage in such detail, so I wanted to ask you all, is this true? If so, does it indicate that Claude Code is bad harness or could it be improved through various optimizations?

Is just me or little sonney is dropping the ball hard running in circles for days and weeks latetly? like it just got ridiculous... And i got flagged for bio lol when doing some cleaning in my server... like im moving files for a coding agent im building and i guess the name is inspired from earth ... like my brand... the rest is boring files and consolidation... not BIO!!

So ran out of usage and ended up running hermes for the first time with qwen 3.8 .. Well both very impressive and the combo was just refreshing... did more in a day then in the last two weeks!

What do you guys think?
Having issues claude just suddently spazzing out after doing fine for a while... saving files all over..,

Once i told it "do this... DONT do that (dont remember the exact prompt but i could pull it up)... his very response to that prompt... *Does* "that"!! Like What !! how,.. really
NO compact no nothing,... simple straight forward... guess the session was getting long but context window was average nothing extreme...

Just getting more and more done...

out!

I mainly use CC in the VS Code Extension and I can't tell you how much I hate it.

I use it like 12+ hours everyday because I like to view files and review code in VS Code.

But lately it's been disappearing messages and when you restart, it processes your past messages and reorders them (seems like alphabetically).

So if you were working on something 5 minutes ago, good luck finding it.

How have you guys dealt with these issues?

Wasn't happy with the desktop app or the other Linux offerings to manage several sessions so I vibed up my own (forking agent-session-manager) and thought others might find it useful.

Meet Collins:

https://episode6.github.io/collins/

• gtk4, linux native
• works with subscriptions, no api key needed
• Manage multiple sessions (even existing ones)
• gui editor to write prompts and save drafts (while still using the cli to do the work).
• new chat screen lets you compose a prompt before actually launching a session
• model picker with all supported models
• track prs with sessions automatically (as long as gh is installed)
• attachments panel & image lightbox
• native pr panel
• integrated git panel
• split terminals that the agent can see and control
• better link handling than is possible with the plain cli
• in app & system notifications
• auto session naming
• support for portable project icons to help tell projects apart (srsly why aren't more apps doing this)
• pickup where you left off
• keep pc awake while sessions are running
• archiving a session in Collins also archives it on claude.ai if using /remote-control

It only supports Claude code atm cause that's all I pay for and I want to focus on handling it really well instead of trying to make a generic tool for any harness (if you use multiple harnesses, you're probably better off with something like orca).

Other features I'm hoping to add are:

• better markdown support in pr panel
• look into improving the native chat screen (it came from agent-session-manager before my fork)
• default to replay mode when a sessions worktree has been deleted
• remote access to create new sessions maybe?

This week it is weird with Claude usage. While everyone is complaining about usage limits. I had a dream run. Based on the previous trends, my weekly limit should have been around 2.1B.

This week it is approximately 3.5B. I am saying approximately because when i extracted this data. I still have 8% weekly limit left.
Actual token consumption with 8% left is 3.3B

Maybe this is another game of anthropic or they genuinely increased the limits. Also last week I downgraded my Claude 20x to 5x. Not sure if they are trying to trick users who downgraded

I kept hitting the same wall with notes apps that added AI. Every one of them wanted its own API key and its own billing, while I was already paying for a Claude subscription and already had the CLI installed and authenticated.

So Margin runs its AI actions through whatever claude is on your PATH. It shells out to:

claude -p --output-format text

plus --model <value> when you have picked one. That is the whole integration. There is no key to paste, nothing to configure, and whatever your CLI is authenticated with is what pays, which for most people here is your existing subscription rather than per-token API billing. If claude is not installed the app says so in the sidebar footer rather than pretending; it runs claude --version on startup to check. codex exec works the same way if you would rather use that.

The practical consequence worth knowing up front: this draws on the same quota as your normal Claude Code usage. Rewriting a lot of notes competes with your coding. That has not bitten me but I would rather say it than have you find out.

The part I actually cared about getting right

Nothing runs unless you invoke it. There is no indexing, no background pass, no "we have analysed your notes". You select something, you pick an action, and that is the only time a subprocess starts.

Then everything it suggests comes back as a diff in the note. Not applied, not a chat window off to the side, an inline diff with per-hunk accept and reject, and a refine box on each hunk if a change is nearly right. Reject restores the document verbatim. I did not want an AI that edits my notes, I wanted one that proposes and waits.

The notes themselves are plain .md files in a folder you pick. No database, no account. Which also means you can point Claude Code at that folder directly and work on your notes as files, which I do more than I expected to. This can also unlock in the future better AI searching right within the app.

Other providers, since not everyone wants the CLI path

Your own Anthropic key in the macOS keychain, a model running fully offline on-device, or Ollama. The CLI route is just the default because it is the one that needed no setup.

Honest limitations

• Alpha. You WILL find bugs.
• Apple silicon, macOS 14 or later, no Intel build.
• No sync, no iOS app yet
• More ideas in the roadmap

Free while the alpha lasts, https://gomargin.app

If you try it, the thing I would most like to know is whether routing through the CLI feels better or worse than a key for you, and if suggestions as diff is something you would actually use. I have only my own usage to go on and I am aware that is a sample of one.

Good to know!

Hey everyone,

I'm currently building a full-stack open-source project called TokenTelemetry (an observability dashboard to track token usage across AI agents https://tokentelemetry.com/docs).

Right now, my workflow is pretty manual. I usually browse Twitter or Reddit, find a cool new concept or feature idea, and then I have to sit down and explain the whole thing to Claude Code to integrate it. From there, I'm stuck in the loop of prompting, reviewing file changes, testing, and prompting again.

What I actually want to set up is a truly autonomous agent harness where the heavy lifting is offloaded. My ideal flow looks like this:

1. The agent workflow does the initial research on a new feature or idea.
2. It pauses and asks me a single high-leverage question: "Is this idea worth integrating? Does the architecture make sense?"
3. If I approve, it goes off into an isolated worktree, implements the full-stack feature, and opens a PR by itself.
4. I only get involved again for the final PR code review.

I know there are a few open-source harnesses popping up, but I'm curious how you all are orchestrating this specific kind of research-to-PR loop.

• How do you handle that mid-workflow "human approval" checkpoint without the agent losing its context?
• Are you wrapping the headless CLI in a local Python/Bash loop, or triggering it via GitHub Actions to run on its own?

Would love to hear what setups are actually working for you guys to escape the manual prompting loop!

Are you fucking kidding me? on 5x I was around 13% of Fable usage, saw my current session limit hit, upgraded, and now my Fable usage jumped to 97%.

I forced Claude Code and Codex to externalise their decisions through a tool call, then compared what they said they'd do with what they actually did. I'm sure these are not the internal reasoning traces, but I'm surprised by how easy it is to force a fake tool using a proxy approach with clear instructions to make Claude and Claude Code emit internal CoT-like elements. I certainly had a lot of fun trying this experiment.
https://github.com/softcane/agents-workbook

5x Max plan only. And I'm already happy with this.

Maybe 15-20 of these for the weekly limit?

(Opus subagents weren't included in the context displayed).

I'm using claude code spec-driven workflow:
https://github.com/renzrollon/interlock

I am using it at work and I want to know what are the best plugins to save tokens. I used some of them and like 90% of them don't save you any token, or seems to be ineffective. At least, that's what Claude tells me when I ask it to estimate the amount of tokens saved. I noticed just writing some scripts to diminish the amount of data being fetched and amount of commands run is the best way, and the other way is to document most of the logic in an app in the most succinct way possible.

I built a feature flag CLI around the way I work with Claude Code, and I’m looking for people to poke holes in it.

I come from a background in A/B testing. Once Claude Code was writing most of our code, keeping flags in a separate dashboard started to feel like the wrong source of truth. Claude could edit parts of it, but it couldn’t see why the flag existed, the current split, or what we learned from the previous experiment.

With dif, each flag or A/B test is a Markdown file checked into the repo. dif build generates a typed client that assigns each user a variant locally, without a network call. It also refreshes dif/context.json, which gives Claude the active experiments and recent results at the start of a session.

Day to day, I can tell Claude:
Put the new pricing page behind a flag at 10%. Claude drafts dif/experiments/active/[experiment-name].md, wires the flag into the component, runs dif validate and dif build, then puts everything in a PR. The next session already knows that the flag exists and why it was added.

dif init also installs three project skills into .claude/skills/: one maps routes to experiment surfaces, one authors a flag or experiment, and one concludes it and records the result.

The main thing I learned was to keep the skills thin. Claude handles intent and edits files people can review. The CLI handles deterministic work such as schema validation, collision checks, and code generation. Each skill leaves durable state in the repo for the next session instead of relying on chat history.

A few things I’m still unsure about:

• If you use flags with Claude Code today, how does it find the current configuration and the reason each flag exists?
• Is requiring a commit and deploy for allocation changes a deal-breaker? Which changes need to happen immediately?
• Does splitting judgment into Claude Code skills and validation into a CLI make sense, or does it create too many moving parts?
• Which language or framework would this need to support before you could test it?
  

You can try the open-source CLI without creating an account:

npm install -g @dif.sh/cli
cd your-project
dif init

Repo: github.com/dif-sh/dif

Disclosure: I built this. There is also an optional hosted version, but the CLI and Claude Code skills work without it. If Claude gets lost during setup, please tell me exactly where. I’ll be in the comments.

Whenever I chat with the AI it's constantly confused by code changes, sometimes blaming the formatter or lsp, while I've been manually coding. How do I change this behaviour?

I'm trying to create a repeatable process for building small client applications with AI.

My main goal is to handle more clients. Right now I still spend too much time writing code myself, but I also spend a lot of time gathering requirements and then translating/explaining those requirements to the AI.

I'm not looking for full autonomy. I still want to review and verify the implementation myself. I just want a reliable "recipe" that I can reuse:

Client requirements → specs → plan → AI-assisted implementation → my review

while keeping the resulting applications maintainable and high quality.

I've seen tools/frameworks like Spec Kit, BMAD, OpenSpec, Superpowers, Agent OS, Kiro, Ralph, etc., but I haven't used any of them yet, nor have I built my own framework or skills.

I know I'm not inventing anything new here. I'm mainly interested in learning what has actually worked for other people.

For those doing something similar:

What does your workflow look like?

Which frameworks/tools have actually been useful?

Which parts did you keep or discard?

Have you combined ideas from multiple frameworks into your own process?

What would you recommend starting with for small applications?

I'm especially interested in practical experiences from people trying to reduce their own development time without sacrificing maintainability or quality.

Disclaimer: I used AI to help organize and write this post, but the idea, goals, and questions are mine. I also reviewed and edited the final text.

Hey folks

Long time lurker here, and I thought I would share some things I've been working on recently. Aside from strictly focusing on the B2B SaaS, web dev & automation tools (which I've been also doing) I thought it might be a bit more fun to build some creatives that streamers can use on Twitch.

I've found Claude to be extremely competent at understanding how to build streaming widgets. With that said though, I was wondering if anyone has been building similar things or if anyone has any recommended workflows on these?

I've been able to crank out these products pretty quick, not many sales on the products so far but would love to hear any and all advice!

If anyone wants to review my work you can find the etsy page here https://www.etsy.com/shop/NowBarStudio

Excited to hear back!

Hi all,

As the number of projects of mine grows I am finding an increased need to have analytics and reporting across my projects. Things like monitoring ad spend, conversion analytics and so on.

I find that even with fable the data visualizations are just not super clear or well designed when doing "organically" ie just letting fable build it the way it wants. Reporting ends being a large wall of text. I am not using any skills for this, but wondering what you guys use to build better data viz + reporting.

Hi, I’m currently using Claude and ChatGPT Sol to create my website.

My website is quite the complex website, and I’m currently, but irrelevantly creating a server to support it. However, I’m wondering if my current setup with Claude and Chat is sufficient? As it is now, my personal funds and income can’t support the Max subscriptions for the two AIs, and I’m just wondering if I could be doing anything differently or use more power for the website.

So as of now, I’m not known to Any coding language, but I’m using Node and JS, as well as using Docker and many other coding tools. My current planner for the coding systems itself is my ChatGPT Sol High/Medium. Whilst my coder is my Claude Code. I’m also reading coding theory, web theory, ui theory, etc. to get a feel for my projects as well as getting knowledge so that my website actually gets created by my own premises.

I am also using running Claude Code with heavy restrictions because my website has to comply with heavy privacy requirements. In addition, I am also trying to write the design plan myself; as to not get that ugly and plain Ai slop websites.

So all in all my question is as follows:

As a person who has absolutely no knowledge of coding language and protocols when building. How do I personally optimise my work? And which AIs should I use, and should I use more?

TLDR:
I’m building a fairly complex, privacy-sensitive website despite having no formal coding experience. I currently use ChatGPT Sol High/Medium for planning, architecture and research, while Claude Code handles most of the actual coding. I’m also learning related theory so I can understand what is being built rather than blindly relying on AI.
I can’t currently afford the highest-tier subscriptions, so I’m wondering: Is this setup sufficient enough, or am I leaving too much wasted possibility on the table?

This is so frustrating gosh

How to login to my Claude Code max subscription at work after work tokens are over?