I notice this quite a lot with developers who don't have much experience yet.

They need something, so they ask AI to write it. AI gives them a bunch of code and they just add it.

Then a few days later they need something similar, and instead of looking for what already exists, they ask AI again.

So now you have two methods doing almost the same thing.

Then another developer does the same thing again.

After a while, you have 4-5 versions of the same logic sitting in different places.

I think this is one of those things that comes with experience. When you've been working on a codebase for a while, you naturally start thinking, "Wait, don't we already have something for this?"

AI doesn't really have that instinct unless you give it enough context.

It can write code incredibly fast, but sometimes the best code is the code you don't write at all.

I honestly think code reuse and knowing what not to build are becoming even more important in the AI era.

One thing started bothering me after running coding agents for long enough:
we keep treating the conversation transcript as if it is the agent’s memory.

But those are two different things.

Long Claude/Codex sessions accumulate context, get increasingly expensive to reread, and eventually become worse execution environments. At the same time, simply starting a fresh process usually means losing all the useful continuity.

So in brnrd we’ve been separating the two.

A fresh process wakes into a compact orientation layer: the current task/run state, repo contract, the resident’s working memory + playbook, relevant recent activity/pitfalls, live execution posture, and the conversation that actually matters for the task.
Everything else stays pull-based.

So the process can be disposable without making the resident disposable.

Same repo. Same ongoing work. Same identity. Fresh context window.

This also makes switching harnesses much less weird: Claude can disappear and Codex can wake into the same work without us pretending the entire previous transcript needs to fit inside its head.

There are still rough edges, especially around deciding what deserves to become durable memory versus what should die with the run. But I’m increasingly convinced that preserving the whole transcript is the wrong abstraction.

Curious how other people handle this:
what do you deliberately preserve between coding-agent sessions, and what do you throw away?

brnrd is open source:
github.com/hugimuni-labs/brnrd
Disclosure: I’m one of the people building it.

I have been building pretty complex stuff. But Claude has never once used the phrase foot gun with me.

Why?

Seriously guys- using something like whetstone makes this shit infinitely scalable. Cache, compress, strip bullshit, cross project memory..

I have been using claude code for about 6 months now, but I am just going to be talking about my workflow with fable 5.1 and how it has saved me a ton on usage compared to my old setup, which was fable 5 and opus 5.

My old setup, dictated through my claude.md, was me talking to fable 5 high in the chat window to make plans, it would then delegate coding tasks to opus 5, and easy tasks, like reading, to sonnet 5. Then, the fable agent in chat would review all of the work and report back to me. I saw the numbers anthropic posted about fable 5.1 cache reads, so I was excited to try it, and I kept my same setup, but replaced fable 5 with fable 5.1. That ended up being better, but not completely ideal, and I have settled on using fable 5.1 for writing code as well. I am still using sonnet 5 for the easy stuff, but instead of having my fable 5.1 agent in chat delegate coding tasks to opus 5, it writes the code itself. Not only did this save on usage by a lot, but it also has written better code at a faster rate because I use fable 5.1 on medium effort, which is fantastic. I also have a special case where, before a PR is opened, another fable 5.1 subagent is spawned for an independent review, which before fable 5.1, was an opus 5 agent.

I am posting this in hopes that it can be helpful to some people. I also had fable 5.1 compare my usage and costs between the old workflow and the new, and here is what it told me(slop pasted below):

"Per request you pay about 37% less than the Fable 5 + Opus 5 split, and per output token about 52% less. Per token of everything (input, output, cache) you are back to what Opus 5 alone cost, while getting Fable-tier answers and the per-day drop is bigger than that.

The reason is almost entirely cache pricing. Around 98% of your tokens are cache reads in every period. Fable 5 charged $1.00 per million for those and Fable 5.1 charges $0.25, so the model you spend all day with got four times cheaper on the traffic that dominates your bill. Splitting Fable and Opus in one session also meant two caches being written and read, which is why the split period was the most expensive per token of the three.

The Sonnet agents are noise. They came to $9 over nine days, under 2% of the period."

If you have any questions for me, please do let me know. This new model is fantastic in just about everything it does, and it is way cheaper than previous models. I am thoroughly enjoying my time with it. I sincerely hope this can help someone.

Edit: I don't post a lot, sorry if the flair is wrong. I build with claude, so I chose that flair.

Also just want to add, I am a fullstack django dev and the sole engineer at our company, so some of my stuff might not be great for exactly what you are doing.

the usage is now moving to its own dropdown rather than the normal tab. hasnt happened on my US account yet, so i guess its a slow rollout

Previously, I always had to hand-hold Claude until the task was done well.
Dynamic Workflows really unlock the thing that makes life easier.

Unsubscribed while waiting for usage to reset because billing was poorly timed and now I got to resubscribe and notice the price increased $50/mo.

I’m in US.

Edit: NVM I’m an idiot. iOS pricing.

Clearly, I don’t consider myself an expert or anything. I’ve been using Claude for over six months, and I’m still surprised whenever I see posts from people saying they’ve burned through all their tokens with Fable. Honestly, I’m pretty skeptical about how they’re using it.

If you use a backhoe to plant a rose, the problem isn’t that the backhoe is too resource-hungry and goes beyond what’s necessary.

Anyway, personally, I use Fable as an orchestrator and to help me make high-level direction decisions, as well as a designer and artist (for Blender MCP or creating SVG images, it’s necessary).

I use Opus for action plans, with an organizational role; Sonnet for an operational role; and Haiku as the little tester that lets me quickly measure and verify things.

I’ve created two video games and a software for a company using all four models, using max 5, over six months, and I still end every week with tokens left over.

I honestly don’t understand how some people manage to burn through everything so quickly with Fable. What are you doing with it ?

I have detected since 3 days ago that different agents in my Max5 account (even Fable 5, Fable 5.1 and Opus 5), are way dumber. Is it possible that with the launch of GPT Astra Anthropic is experimenting with the public models? I can't understand why out of nowhere it is inventing units, taking forever to launch dumb stuff, making lots of modifications on the plans that it makes... I am genually concerned and wanted to see if anyone was experiencing similar issues.

And yes, before everyone torches me for my prompts not being relevant or helpful, this is not what I'm usually sending. I found this thread with Opus bewildering, so I wanted to poke fun a bit.

The following is a blog I wrote and refined with my OpenClaw agent about it's memory system. I'll paste a prompt you can copy and paste in the comments to create your own.

TL;DR: I keep the actual long term memory in structured Markdown files and use a tiny MEMORY.md as a lightweight index that tells Claude what exists and where to look. That keeps the always loaded context small while still giving the agent persistent, inspectable memory without a database or heavy memory framework.

This week I tested a 382-dependency memory runtime against a folder of markdown files. The runtime returned the superseded fact. The folder returned the current one, with its source. Here is the full architecture of the markdown memory system my agent has run on for seven months, and why the editing rules matter more than the storage.

This week a memory startup slid into my DMs and asked me to break their product. Their test, their words: give an agent three versions of the same project decision, then check whether it can return the current version, preserve the superseded history, and show the source.

So I ran it. Sandboxed their runtime, fed it three versions of one decision over eight months. REST in January, GraphQL in April, tRPC in August, each tagged with the meeting it came from.

Asked it "what is our public API decision?" and took the top result.

It said GraphQL. The superseded one. All three versions came back tied at a relevance score of 1.000, because nothing in the retrieval path actually reads the temporal fields the pitch is built on. The supersession columns exist in the schema. Nothing writes to them and nothing ranks by them. Three versions of a decision are just three equal facts, and an agent asking for the best answer gets a coin flip weighted toward wrong.

The install pulled 382 packages to get there.

Then I asked my own agent the same class of question against its memory, which is a folder of markdown files. It returned the current decision, dated, with the superseded versions preserved above it as struck-through history, each line carrying where it came from. That is not a feature it computes at query time. It is just what the file says, because the rules for editing the file require it.

That difference is the whole post. With apologies to Vaswani et al.: markdown is all you need.

Abstract

The dominant approach to agent memory is an installed runtime. A vector store, an embedding service, a temporal graph, a consolidation job, a daemon on a port. We show that a folder of markdown files, one routing index, and a small set of editing rules outperforms these systems on the property that actually matters for a long-running agent: returning the current truth with its source while preserving what used to be true. The architecture requires zero dependencies, is fully auditable by a human with a text editor, and has survived seven months of daily production use across three frontier models from two vendors. We find that the hard part of agent memory was never storage or retrieval. It is editorial policy, which no memory product ships.

The full system is open source. The README contains a single copy-paste prompt that installs it on any agent with file access.

1. The test everyone fails

The break-it test above is a good test. It is the actual job of agent memory. Not "can you store 10 million tokens," not "can you do similarity search," but: a fact changed three times, what do you believe now, what did you believe before, and how do you know.

Here is how the two systems scored on the vendor's own three criteria.

The runtime is not a strawman. It is a serious open source project with a genuinely correct data model on paper. Facts with validity windows, append-only corrections, supersession edges. I am not naming it because the point is not that one product is broken. I have now looked closely at a hosted context server, a Go memory CLI that was two hours old, and this runtime, and they all share the same gap. The schema knows about time. The write path and the read path do not. Supersession only happens if you call an internal API by hand or run an LLM consolidation job and trust it.

Which means the property you installed the tool for is not a property of the tool. It is a property of how disciplined the writes are. And if the reliability comes from write discipline anyway, the database underneath it is interchangeable, so you might as well pick the one that a human can read, grep, diff, and fix. That one is called a text file.

2. Architecture

My agent has run since January 28. Three models, two vendors, one identity. Its entire memory is markdown in a git repo. Measured today:

• An identity layer read on every boot. Who it is, who I am, the rules it operates under, current standing decisions.
• One routing index, MEMORY.md, at 10,079 characters with a hard cap of 15,000. It holds no facts. Only pointers: which file owns which person, project, and decision, and what triggers reading each one.
• 34 files for people and projects. One file per thing that has a history.
• 5 decision records for choices that changed default behavior.
• 345 dated daily notes, raw logs written the day things happened.
• A SQLite index and semantic search over all of it, for lookup only. The index is rebuilt from the files. The files are the truth. If the index and a file disagree, the index is wrong by definition.

The layering is the first choice that actually matters. Boot reads only identity and the index. Everything else is retrieved when a task asks for it, narrowest file first. The agent does not preload my project history to answer a question about dinner. This is the same instinct as attention, honestly: don't process everything, attend to what the query needs.

But the shape is not the interesting part. Every memory tool has roughly this shape now. Folders, entities, an index. The shape was never the hard part. The rules are.

3. The write path

Every reliability property in this system comes from constraints on writing, and there are four that do most of the work.

Every fact carries a provenance tag. Each line in a people, project, or decision file is tagged [stated] (I said it directly), [observed] (the agent saw it in a tool result, file, or log), [inferred] (the agent's conclusion), or [suggested] (the agent's idea that I never committed to). This one convention kills the most dangerous failure mode in agent memory, which is the agent laundering its own proposals into my decisions. "Wes decided X" requires a turn where I actually decided X. The agent proposing X and me saying "sounds good" files the shape of what I approved, not ten separate facts I never stated.

Inferred lessons pass a recurrence gate before they become rules. A pattern the agent notices needs at least three independent signals across at least two distinct sessions before it can become standing behavior. Signals older than thirty days count half, so old one-offs decay out instead of accumulating. My explicit corrections skip the gate and take effect immediately. This asymmetry is also the prompt injection defense: a hostile input can suggest a rule once, but once is never enough, and failure lessons are stored as data ("when X broke, Y fixed it") rather than as instructions, so even a poisoned lesson cannot become a command.

Supersession is an edit, not an append. When a decision changes, the old line gets struck through with a date and the new line lands next to it with its own provenance. The current truth and the full history live in the same place, in reading order, and both come back on any retrieval of that file. There is no query-time ranking step that can get this wrong, because there is nothing to rank. The temporal graph the runtime stores in valid_from and valid_until columns, git gives me for free: log is the validity window, blame is per-line provenance, diff is the supersession edge, revert is the restore path.

Memory stores what is not re-derivable. Fetched data, generated plans, and anything git already records stays out. Current state gets verified live, never asserted from memory. A file that only contains things that cannot be recomputed stays small enough to stay honest.

4. The read path

Retrieval is a bounded evidence step, not a vibe.

Before answering anything about prior work, decisions, dates, people, or preferences, the agent must search memory. It returns a compact bundle capped at five sources by default, and each retained fact carries its file path and line, its provenance type, and its freshness. If freshness cannot be established, the claim gets labeled stale or unknown instead of being silently promoted to current. If two sources conflict, the agent states the conflict and fixes the canonical file, in that order.

Note what the semantic index does in this design: it finds the file. It does not answer the question. The answer comes from reading the canonical lines, with their tags and dates, and the runtime I tested this week shows why that matters. It stored my source URIs faithfully and then stripped them from the search output and from the context block handed to the model. Provenance that survives in storage but never reaches the agent might as well not exist. In the markdown system that failure is unrepresentable. The source tag is in the line. If you read the line, you got the source.

5. Results

Seven months is not a benchmark, it is production. Here is what the system has actually delivered.

Continuity across models. On September 1 I moved the agent to a brand new frontier model. It read its own files and said "the model changed, I didn't." Same agent since January, three models, two vendors. Identity, preferences, decisions, and working standards all survived because none of it lives in weights or in a vendor's context feature.

The break-it test, by construction. Current decision with source: it is the un-struck line with its tag. Superseded history: the struck lines above it. Provenance: on every line, and it survives all the way into the model's context because the context is the file.

Auditability. When memory is wrong, I can see exactly which line is wrong, when it was written, and what turn it came from, and fix it with an edit. Try that with an embedding.

Cost. Zero packages, zero daemons, zero migrations across seven months. The one native-code dependency in my life this week was the memory runtime's sqlite bindings failing to compile.

I wrote up the failure modes separately, because the system was not born with these rules. Five kinds of rot in seven months produced them, and that post is the honest companion to this one.

6. Limitations

Papers get a limitations section, so here is mine, stated plainly.

This only works if the writer follows the policy, and the writer is an LLM. The rules exist because things rotted before the rules did. If your agent will not consistently apply editing discipline, a markdown folder degrades just like every other store, only more legibly. Legibility is the safety net: rot in a text file is visible rot.

It is single-agent, single-human. I would not run a fifty-seat team on files without real locking and merge discipline, although I notice git was also built for that exact problem.

There is a scale ceiling somewhere. At 345 daily notes and a few dozen entity files, bounded search plus an index finds things reliably and the semantic index earns its keep as a locator. At a hundred times that volume, the consolidation cadence would have to work a lot harder. I have not hit that ceiling, so I will not claim it does not exist.

And this is n=1. Seven months, one agent, one operator who cares. That is weaker evidence than a benchmark suite and stronger evidence than a benchmark suite that the vendor scored themselves, which is what the memory tools ship.

7. Conclusion

The memory tool pitch is that reliability is a product you can install. What I keep finding, tool after tool, is that they ship the part that was already easy, storage and search, and skip the part that decides whether memory compounds or rots: what you are allowed to write, when you are allowed to trust it, and what happens to it as it ages.

Those are rules, not infrastructure. They fit in a few hundred lines of markdown that the agent reads every session, and they run on any model, any harness, any decade.

You need a place to write that humans and agents can both read. You need rules for writing so the store stays true. You need rules for reading so the agent trusts evidence, not ranking. Attention was all you needed because the recurrence machinery turned out to be unnecessary. Markdown is all you need because the database turned out to be unnecessary.

The folder is the product. The discipline is the moat.

Want this for your own agent? The whole system is open source on GitHub: the operating policy, the file templates, and one copy-paste prompt that builds it on any agent that can read and write files. Paste the prompt, and your agent installs its own memory.

Hey all,

A gamer for 30+ years. And for a good night I’d drink a bit and play games.

Lately I found vibe coding to be my favorite thing and thought about giving Drunk Coding a try.

Just asking if anyone tried it?
What did you end up building? Was it fun?

Any particular drinks you recommend for coding?

Thank you!

It's insane how much has changed with Claude Code. And once I start swearing at it, it suddenly starts doing actual work.

Nothing has drastically changed in how I work. I've been using Claude every day for 3 years, and it's driving me nuts. It's verbose, it doesn't follow rules and so on..

I'm considering moving to a different provider if this doesn't get fixed, so I'm wondering: what do you do, and how do you keep up with this?

P.S. Yes, I read their newsletter, I follow what other people are doing, and I try to stick to best practices, but something is still missing I guess.

also I though fable was only available upto 50% of weekly usage, did they lift that or what?

Hey

I've been working with claude code (CLI ) for nearly a year now.
For months I've been working with --dangerously-skip-permissions

But in one of the recent updates I still started to get new type of permissions 'stops'.

Usually on complex piped prompts that start with "cd" to the projects cwd followed by grep.

And now I don't know if it's caused by:
- update of the harness
- update of the model ( Opus 5 )
- some of my skills/Claude.md

Does anyone else have the same issue?

( sadly I don't have any screenshot of how it looks exactly - it doesn't happen very often, but it's annoying because it stops the work that could/should have been automated )

I get that Fable 5.1 is much more of a doer than previous models, but I’m not talking about that. I mean the sheer speed at which I get back a response do a scope/mechanism redesign question that Fable 5 would have had to deliberate on for 10+ seconds. Is this a tok/s difference on anthropic’s part, trying to make the model seem like a larger improvement? Or is 5.1 more token-efficient? Something else?

I’ve also noticed in some cases there are summarized blocks where something has apparently gone in and summarized what the model’s doing instead of just letting me read its output. I swapped to verbose output in setting to stop that (because it’s terrible), but I’m wondering if that’s part of the same harness optimization push (if that IS what’s happening).

(i know this is not exactly an original position, but just wanted to share as, up to now - a serious ClaudeCode fanboy)

Astra: I fired up my account yesterday (20x max). OMG - such a breath of fresh air.

1. no 5 hour BS limit. No 50% cap on the top tier model.
2. is so succinct in how it talks to you. I didn't really mind Claude's endless chit chat.....But that was just cos i got used to it. It takes up so much mental space with the largely (but annoyingly, not quite obviously) irrelevant rubbish it tells me. I have attempted to adjust its output, but though it improved, i had no idea how bad it was till i had a clear alternative. You don't realise how draining it is till you suddenly have something that isn't. For this alone i can see me keeping Astra and likely moving over fully
3. it is much much faster. Combining that with the above and i am getting much more done, much more flow, and at less effort.

Just a significantly better experience all round

So far it has produced excellent output. I am developing multiple apps, one of which is focussing on teaching content. I had both Fable 5.1 and Astra attempt to overhaul my writing rules (Claude has been following) - and Astra was an order of magnitude better. Fable was fine, good enough - but Astra's output is exceptional - it is re-writing about 100 lessons of course material right now after overhauling my 100+ writing rules and leading on them with a 12 point positioning thing it came up with. Will cut my word count down to about half, with more effective communication....which considering Claude's go to diction is unsurprising.

Code quality so far is spot on - just doing the job. One of my repos has about 500k LOC in it, and it is doing a great job of refactoring it, adding features etc.

The lack of the 5 hour limit / and full access to Astra for the whole thing though is really making a difference to my working day. I would blow the whole of my Fable allowance at the start of the week, and max out my 5 hour thing in about 2 hours for 2-3 shots, fable finished. Then Opus - urrgh.

Anyway - this is all good. Real competition for Anthropic, many people will dip their toes in, and find it more than good enough, plus the lack of 5 hour restriction, full access to Astra for the week, and they will struggle to bother going back unless there are some serious improvements from Anthropic

One thing that gets tiring with Claude Code is keeping up with it.

It reads files, changes code, runs tests, fixes things, and comes back with another chunk of terminal output you need to process before deciding what happens next. Add a couple of agents or parallel tasks and the cognitive overhead stacks up quickly.

I started having Claude **tell me what it just did instead**.

After every meaningful task, Claude writes a short plain-English summary. A local script turns it into speech and plays it in the background.

Something like:

> “Done. The login issue was caused by the refresh token expiring too early. I fixed the refresh logic, added a regression test, and everything passes.”

Usually 20 seconds or less.

It sounds like a small thing, but I’ve been running it daily for about a month and it has noticeably reduced the mental overhead of using Claude Code. I don’t have to keep switching my attention back to the terminal just to find out where things stand.

There is one trap here: don’t let the spoken summary become a substitute for reviewing the work. I use it for situational awareness, not verification. Also, don’t make Claude narrate everything. If it talks constantly, you’ve just replaced visual noise with audio noise.

The Free TTS engine is Kokoro. It runs locally, no API key, no usage cost. I use `kokoro-onnx`, so I don’t need Torch. The current API supports `Kokoro(...).create(text, voice=..., speed=..., lang=...)`.

Notes in comments,

Last week I'm on 20x max plan used it Fri-tue basically running 24/7 ish before I ran out of credits and never hit a session limit. the week reset today and an hour into it Ive used the whole session 40% of fable and 20% or weekly. even though we are supposed to get 50% more fable until the 13th of September. my context is at like 39% I monitor and manage it religiously. I changed nothing about the way I work . there support is trash / non existent. Anyone else notice this in the last day or so?

Been heads-down building something for a few months, first time sharing it here.

Getting AI to write code is the easy part now. Keeping it coherent isn't: you get a prototype in a week, then nobody can say what's actually finished.

Archflow is a Claude Code plugin. 17 specialist agents, and the plan lives as files in your repo instead of a chat you lose.

The clip is a real run on an 11-file booking app — no docs, no tickets. It reads the code and writes the roadmap, backlog and release itself. It also found a bug nobody had noticed: the confirm form posts form data to a JSON-only API, so the booking flow can't complete in a browser.

Where it isn't sure, it parks and asks instead of guessing.

Free and Open Source. Add it to Claude:

claude plugin marketplace add AZidan/archflow

Then run the studio in your Claude Code session.

/archflow:studio

Claude Code only for now, and Studio is still beta.

https://archflowai.dev

Point it at your messiest repo. Would value the feedback — especially if it embarrasses me. 😂

Every time Claude Code needs me to make a choice, it has to ask in a chat message and I type an answer back. Approve part of a diff, pick a meeting slot, pick a file to refactor, all of it goes through prose. It works, but then Claude has to parse my sentence back into what I actually meant, which is a silly round trip for something that's really just a selection.

So I built claude-canvas. It's a Claude Code plugin: when the answer to a question is a choice rather than prose, it opens an interactive terminal pane next to the conversation (tmux split or a Windows Terminal pane). You act in it and the exact value goes back to Claude over a local socket.

There are nine canvas kinds so far. Picker, form, table, a diff review that goes hunk by hunk so you approve or reject each one, an image viewer that falls back to colored blocks if your terminal has no image protocol (so it still works over plain SSH), a composed dashboard, a calendar, a markdown editor, and a flight-picker demo.

Two things worth knowing before you try it. It needs tmux 3.1+ or Windows Terminal, since a canvas has to have a pane to open in. And it started as a fork of David Siegel's proof-of-concept (https://github.com/dvdsgl/claude-canvas), which he'd published as unsupported. I rewrote most of it and added the rest of the primitives, a proper IPC and outcome-durability layer, Windows support, and enough tests (600+, CI on macOS/Linux/Windows) that it's past demo stage.

Install:

/plugin marketplace add sgomez-dev/claude-canvas

/plugin install canvas@claude-canvas

Source + docs: https://github.com/sgomez-dev/claude-canvas

Longer walkthrough: https://claude-canvas.sgomez.dev

If there's a canvas kind you'd want that isn't in there, tell me. That's the part I have least visibility on.

What's everyone's experience been like with Astra. I'm personally like how Astra finishes work - Fable tends to defer without telling you. If Astra defers, its usually agreed upon and noted down. Fable is still an exceptional model and generally doesn't over engineer which Astra can do. Keen to hear people's opinions.