I'm not sure how many HN users frequent other places related to agentic coding like the subreddits of particular providers, but this has got to be the 1000th "ultimate memory system"/break-free-of-the-context-limit-tyranny! project I've seen, and like all other similar projects there's never any evidence or even attempt at measuring any metric of performance improved by it. Of course it's hard to measure such a thing, but that's part of exactly why it's hard to build something like this. Here's user #1001 that's been told by Claude "What a fascinating idea! You've identified a real gap in the market for a simple database based memory system to extend agent memory."
I feel like so many of these memory solutions are incredibly over-engineered too.
You can work around a lot of the memory issues for large and complex tasks just by making the agent keep work logs. Critical context to keep throughout large pieces of work include decisions, conversations, investigations, plans and implementations - a normal developer should be tracking these and it's sensible to have the agent track them too in a way that survives compaction.
Great advise. For large plans I tell the agent to write to an “implementation_log.md” and make note of it during compaction. Additionally the agent can also just reference the original session logs.
Which of the 1000 is your favorite? There does seem to be a shallow race to optimizing xyz benchmark for some narrow sliver of the context problem, but you're right, context problem space is big, so I don't think we'll hurry to join that narrow race.
None, that's what I'm trying to say. My favorite is just storing project context locally in docs that agents can discover on their own or I can point to if needed. This doesn't require me to upload sensitive code or information to anonymous people's side projects and has and equivalent amount of hard evidence for efficacy (zero), but at least has my own anecdotal evidence of helping and doesn't invite additonal security risk.
People go way overboard with MCPs and armies of subagents built on wishes and unproven memory systems because no one really knows for sure how to get past the spot we all hit where the agentic project that was progressing perfectly hits a sharp downtrend in progress. Doesn't mean it's time to send our data to strangers.
It sounds like you've used quite a few. What programs are you expecting? Assuming you're talking about doing some inference on the data? Or optimizing for some RAG or something?
Nah, just another one of those spam bots on all the small-business, finance and tradies sub-reddits: "Hey fellow users, have you ever suffered from <use case>? What is the problem you want solved? Tell me your honest opinions below!"
Why should he put effort into measuring a tool that the author has not? The point is there are so many of these tools an objective measure that the creators of these tools can compare against each other would be better.
So a better question to ask is - Do you have any ideas for an objective way to a measure a performance of agentic coding tools? So we can truly determine what improves performance or not.
I would hope that internal to OpenAI and Anthropic they use something similar to the harness/test cases they use for training their full models to determine if changes to claude code result in better performance.
Well, if I were Microsoft and training co-pilot, I would log all the <restore checkpoint> user actions and grade the agents on that. At scale across all users, "resets per agent command" should be useful. But then again, publishing the true numbers might be embarrassing..
I imagine HN, despite being full of experts and vet devs, also might have a prevalent attitude of looking down on using tools like MCP servers or agentic AI libraries for coding, which might be why something like this advertised seems novel rather than redundant.
> I imagine HN, despite being full of experts and vet devs, also might have a prevalent attitude of looking down on using tools like MCP servers or agentic AI libraries for coding, which might be why something like this advertised seems novel rather than redundant.
I’m not sure where the ‘despite’ comes in. Experts and vets have opinions and this is probably the best online forum to express them. Lots of experts and vets also dislike extremely popular unrelated tools like VB, Windows, “no-code” systems, and Google web search… it’s not a personality flaw. It doesn’t automatically mean they’re right, either, but ‘expert’ and ‘vet’ are earned statuses, and that means something. We’ve seen trends come and go and empires rise and fall, and been repeatedly showered in the related hype/PR/FUD. Not reflexively embracing everything that some critical mass of other people like is totally fine.
I think maybe the point they were trying to make is that despite people on HN being very technically experienced, skepticism and distrust of LLM-assisted coding tools may have prevented many of them from exploring the space too deeply yet. So a project like this may seem novel to many readers here, when the reality for users who've been using and following tools like Claude Code (and similar) closely for a while now is that claims like the one's this project is making come out multiple times per week.
What is the purpose of a separate /handoff and /sync command? It seems like handoff could just write learnings straight to their final destinations without needing an .inbox.md buffer in-between.
I like to read and review what was captured in .inbox.md before it is committed and synced across my knowledge base. Allows me to catch mistakes, tweak preferences, add context and decide whether something is actually worth pushing.
I will typically make multiple '/handoff's per day as I use Claude code whereas I typically use '/sync' at the end of the day to organise them all at once.
The extention Cline has a "memory bank" feature. It's just a markdown you add as an instruction. Works well for me. Worked with agents.md as well so not just with the Cline extention. Pretty much the same idea.
Your approach essentially matches mine, but I call them plans. I agree with you that the other tools don't seem to add any value compared to this structure.
I think at this point in time, we both have it right.
I struggle with these abstractions over context windows, esp when anthropic is actively focused on improving things like compaction, and knowing the eventual* goal is for the models to yave real memory layers baked in. Until then we have to optimize with how agents work best and ephemeral context is a part of that (they weren’t RL’d/trained with memory abstractions so we shouldn’t use them at inference either). Constant rediscovery that is task specific has worked well for me, doesn’t suffer from context decay, though it does eat more tokens.
Otherwise the ability to search back through history is a valuable simple git log/diff or (rip)grep/jq combo over the session directory. Simple example of mine: https://github.com/backnotprop/rg_history
There is certainly a level where at any time you could be building some abstraction that is no longer required in a month, or 3.
I feel that way too. I have a lot of these things.
But the reality is, it doesn't really happen that often in my actual experience. Everyone is very slow as a whole to understand what these things mean, so far you get quite a bit of time just with an improved, customized system of your own.
My somewhat naive heuristic would be that memory abstractions are a complete mistep in terms of optimization. There is no "super claude mem" or "continual claude" until there actually is.
I tend to agree with you, however compacting has gotten much worse.
So... it's tough. I think memory abstractions are generally a mistake, and generally not needed, however I also think that compacting has gotten so wrong recently that they are also required until Claude Code releases a version with improved compacting.
But I don't do memory abstraction like this at all. I use skills to manage plans, and the plans are the memory abstraction.
But that is more than memory. That is also about having a detailed set of things that must occur.
I understand the use case for plannotator. I understand why you did it that way.
I am working alone. So I am instead having plans automatically update. Same conception, but without a human in the mix.
But I am utilizing skills heavily here. I also have a python script which manages how the LLM calls the plans so it's all deterministic. It happens the same way every time.
That's my big push right now. Every single thing I do, I try to make as much of it as deterministic as possible.
Is anyone else just completely overwhelmed with the number of things you _need_ for claude code? Agents, sub agents, skills, claud.md, agents.md, rules, hooks, etc.
We use Cursor where I work and I find it a good medium for still being in control and knowing what is happening with all of the changes being reviewed in an IDE. Claude feels more like a black box, and one with so many options that it's just overwhelming, yet I continue to try and figure out the best way to use it for my personal projects.
Claude code suffers from initial decision fatigue in my opinion.
I just take a grug brain approach. I do touch CLAUDE.md and then just explain how the code/files/project spec work, like I'm writing a slack message or email to a really smart colleague, and then let it rip, always using biggest model with thinking on. If something consistently goes wrong I add more to CLAUDE.md or even better, have Claude Code just update CLAUDE.md itself with the new issue explained. I'm probably 3 months behind what you could get with absolute SOTA practices but it still works so well that I'm amazed and amused on a daily, if not hourly, basis.
I'm in Claude Code 30+ hr/wk and always have a at least three tabs of CC agents open in my terminal.
Agree with the other comments: pretty much running vanilla everything and only the Playwright MCP (IMO way better than the native chrome integration) and ccstatusline (for fun). Subagents can be as simple as saying "do X task(s) with subagent(s)". Skills are just self @-ing markdown files.
Two of the most important things are 1) maintaining a short (<250 lines) CLAUDE.md and 2) having a /scratch directory where the agent can write one-off scripts to do whatever it needs to.
Not memory, I just instruct it to freely experiment with temporary scripts and artifacts in a specific folder.
This helps it organize temporary things it does like debugging scripts and lets it (or me) reference/build on them later, without filling the context window. Nothing fancy, just a bit of organization that collects in a repo (Git ignored)
When I'm coding I have about 6 instances of VSCode on the go at once; each with their own worktree and the terminal is a dangerous cc in docker. most of the time they are sitting waiting for me. Generally a few are doing spec work/reporting for me to understand something - sometimes with issue context; these are used to plan or redirect my attention if I might've missed something. A few will be just hacking on issues with little to no oversight - I just want it to iterate tests+code+screenshots to come up with a way to do a thing / fix a thing, I'll likely not use the code it generates directly. Then one or two are actually doing work that I'll end up PR'ing or if I'm reviewing they'll be helping me do the review - either mechanically (hey claude, give me a script to launch n instances with a configuration that would show X ... ok, launch them ... ok, change to this ... grab X from the db ... etc.) or insight based (hey claude, check issue X against code Y - does the code reflect their comments; look up the docs for A and compare to the usage in B, give me references).
I've TL'd and PM'd as well as IC'd. Now my IC work feels a lot more like a cross between being a TL and being a senior with a handful of exuberant and reasonably competent juniors. Lots of reviewing, but still having to get into the weeds quickly and then get out of their way.
From personal experience, most of my time in Claude Code is spent experimenting, iterating, and refining approaches. The amount of code it produces as it relates to time spent working on it tends to be pretty logarithmic in practice.
you really don't need any of this crap. you just need Claude Code and CLAUDE.MD in directories where you need to direct it. complicated AI set ups are mid curve
It doesn’t require any major improvement to the underlying model. As long they tinker with system prompts and builtin tools/settings, the coding agent will evolve in unpredictable ways out of my control
That's a rational argument. In practice, what we're actually doing for the most part is managing context, and creating programs to run parts of tasks, so really the system prompts and builtin tools and settings have very little relevance.
i don't understand this mcp/skill distinction? one of the mcps i use indexes the runtime dependency of code modules so that claude can refactor without just blindly grepping.
how would that be a "skill"? just wrap the mcp in a cli?
fwiw this may be a skill issue, pun intended, but i can't seem to get claude to trigger skills, whereas it reaches for mcps more... i wonder if im missing something. I'm plenty productive in claude though.
So MCPs are a bunch of, essenntially skill type objects. But it has to tell you about all of them, and information about all of them up front.
So a Skill is just a smaller granulatrity level of that concept. It's just one of the individual things an MCP can do.
This is about context management at some level. When you need to do a single thing within that full list of potential things, you don't need the instructions about a ton of other unrelated things in the context.
So it's just not that deep. It would be having a python script or whatever that the skill calls that returns the runtime dependencies and gives them back to the LLM so they can refactor without blindly greping.
In our experience, a lot of it is feel and dev preference. After talking to quite a few developers, we've found the skill was the easiest to get started with, but we also have a CLI tool and an MCP server too. You can check out the docs if you'd prefer to try those - feedback welcome: https://www.ensue-network.ai/docs#cli-tool
I like the finetuning aspect to it quite a lot. It makes sense to me. What I achieved now is a very streamlined process of autonomous work of an agent, which can more and more often be simply managed than controlled on a code review level basis for everything.
I agree that this level of finetuning feels overwhelming and might let yourself doubting whether you do utilize Claude to its optimum and the beauty is, that finetunging and macro usage don't interfere, when you stay in your lane.
For example I now don't use the planing agent anymore instead incorporated this process into the normal agents much to the project's advantage. Consistency is key. Anthropic did the right thing.
Codex is quite a different beast and comes from the opposite direction so to say.
I use both, Codex and Claude Opus especially, in my daily work and found them complementary not mutual exclusive. It is like two different evangelists who are on par exercising with different tools to achieve a goal, that both share.
It feels like Claude is taking more of the Android approach of a less opinionated, but more open stack, so people are bending it to the shape they want to match their workflow. I think of the amnesia problem as pretty agent-agnostic, though, knowing what happens while you're delivering product is more of an agent execution layer problem than a tool problem, and it gets bigger when you have swarms coordinating - Jaya wrote a pretty good article about this https://x.com/AustinBaggio/status/2004599657520123933?s=20
You say "My Claude Code Setup" but where is the actual setup there? I generally agree with everything about how LLMs should be called you say, but I don't see any concrete steps of changing Claude Code's settings in there? Where are the "35 agents. 68 skills. 234MB of context."? Is the implementation of the "Layer 4" programs intended to be left to the reader? That's hardly approachable.
I just don't have much faith in "if you're doing it right the results will be magically better than what you get otherwise" anymore. Any single person saying "the problems you run into with using LLMs will be solved if you do it my way" has to really wow me if they want me to put in effort on their tips. I generally agree with your why of why you set up like that. I'm skeptical that it will get over the hump of where I still run into issues.
When you've tried 10 ways of doing it but they all end up getting into a "feed the error back into the LLM and see what it suggests next" you aren't that motivated to put that much effort into trying out an 11th.
The current state of things is extremely useful for a lot of things already.
That's completely fair, I also don't have much faith in that anymore. Very often, the people who make those claims have the most basic implementation that barely is one.
I'm not sure if the problems you run into with using LLMs will be solved if you do it my way. My problems are solved doing it my way. If I heard more about your problems, I would have a specific answer to them.
These are the solutions to where I have run into issues.
For sure, but my solutions are not feed the error back into the LLM. My solutions are varied, but as the blog shows, they are move as much as possible into scripts, and deterministic solutions, and keep the LLM to the smallest possible scope.
The current state of things is extremely useful for a subset of things. That subset of things feels small to me. But it may be every thing a certain person wants to do exists in that subset of things.
It just depends. We're all doing radically different things, and trying very different things.
I certainly understand and appreciate your perspective.
My basic problem is: "first-run" LLM agent output frequently does one or more of the following: fails to compile/run, fails existing test coverage, or fails manual verification. The first two steps have been pretty well automated by agents: inspect output, try to fix, re-run. IME this works really well for things like Python, less-well for things like certain Rust edge cases around lifetimes and such, or goroutine coordination, which require a different sort of reasoning than "typical" procedural programming.
But let's assume that the agents get even better at figuring out the deal with the more specialized languages/features and are able to iterate w/o interaction to fix things.
If the first-pass output still has issues, I still have concerns. They aren't "I'm not going to use these tools" concerns, because I also sometimes write bugs, and they can write the vast majority of code faster than I can.
But they are "I'm not gonna vibe-code my day job" concerns because the existence of trivially-catchable issues suggests that there's likely harder-to-catch issues that will need manual review to make sure (a) test coverage is sufficient, (b) the mental model being implemented is correct, (c) the outside world is interacted with correctly. And I still find bugs in these areas that I have to fix manually.
This all adds up to "these tools save me 20-30% of my time" (the first-draft coding) vs "these agents save me 90% of my time."
So I'm kinda at a plateau for a few months where it'll be hard to convince me to try new things to try to close that 20-30% -> 90% number.
I experience the same things. What I’ve found is there is no issue I can’t solve so it doesn’t repeat.
The real issue is I don’t know the issues ahead of time. So each experience is an iteration stopping things I didn’t know would happen.
Thankfully, I’m not trying to sell anyone anything. I don’t even want people to use what I use. I only want people to understand the why of what I do, and how it adds me value.
I think it’s important to understand this thing we use as best we can.
The personal value you can get, is entirely up to your tolerance for it.
Damn, it really is all just vibes eh? Everyone just vibes their way to coding these days, no proof AI is actually doing anything for you. It's basically just how someone feels now: that's reality.
In some sense, computers and digital things have now just become a part of reality, blending in by force.
I mean, it’s not vibes. I make real projects, and the failures of AI doing it force me to make fixes so that it only ever fails doing that thing once. Then it no longer fails to do that thing.
But the things I am doing might not be the things you are doing.
If you want proof, I intend to release a game to the App Store and steam soon. At that point you can judge if it built a thing adequately.
No offense intended, I don't even know you at all, but I see people claim things like you did so often these days that I begin to question reality. These claims always have some big disclaimer, as yours does. I still don't know a single personal acquaintance who has claimed even a 2x improvement on general coding efficiency, not even 1.5x in general efficiency. Some of my coworkers say AI is good for this or that, but I literally just waste my time and money when I use it, I've never gotten good results or even adequate results to continue trying. I feel like I am taking crazy pills sometimes with all of the hype!
I hope you're just one of the ones who figured it out early and all the hype isn't fake bullshit. I'd much rather be proven wrong than for humanity to have wasted all this time and resources.
I think the correct approach is to be skeptical. You should push back.
I think of this stuff as trivial to understand from my point of view. I am trying to share that.
I have nothing to sell, I don’t want anyone to use my exact setup.
I just want to communicate the value as I see it, and be understood.
The vast majority of it all is complete bullshit, so of course I am not offended that I may sound like 1000 other people trying to get you to download my awesome Claude Code Plugins repo.
Claude Code is better out of the box, so all that other stuff is orthogonal or optional. If you eg want to give your agent access to your company’s Notion docs you need a skill.
I use both Cursor and Claude Code in VS Code at work (so I get similar control as Cursor). I don’t really use Claude Code any differently than cursor. People way over complicate it.
It is overwhelming. We have support for Cursor mcp as well, but you lose a lot of the auto-magic stuff you get with the Claude Code plugin. Unfortunately, skills are pretty sticky to the Claude Code stack. It is kind of the vim of AI coding agents. . . One of the goals for this tool was to address context management in a single place. i.e instead of setting up all of the rules, claude.md, and skill.md you just semantic query a specific namespace in your knowledge base.
You don't need all that, just have Claude write the same documentation you would (should) write for any project. I find it best to record things chronologically and then have Claude do periodic reviews of the docs and update key design documents and roadmap milestones. The best part is you get a written record of everything that you can review when you need to remember when and why something changed. They also come in handy for plan mode since they act as a guide to the existing code.
The general process feels very much like having kids over for a birthday party. Except you have to get them all to play nice and you have no idea what this other kid was conditioned on by their parents. Generally it would all work fine, all the kids know how the party progresses and what their roles are — if any.
But imagine how hard it would be if these kids had short term memory only and they would not know what to focus on except what you tell them to. You literally have to tell them "Here is A-Z pay attention to 'X' only and go do your thing". Add in other managers for this party like a caterer, clowns, your spouse and they also have to tell them that and remember, communicate what other managers have done. No one has solved for this, really.
This is what it felt like in 2025 to code with LLMs on non trivial projects, with some what of an improvement as the year went by. But I am not sure much progress was made in fixing the process part of the problem.
Honestly, very reasonable ask, you're not the first person to ask for a self-hosted version. We have a privacy policy we've drafted that is up-to-date with the current version of the product https://www.ensue-network.ai/privacy-policy.
The project is still in alpha, so you could shape what we build next - what do you need to see, or what gets you comfortable sending proprietary code to other external services?
> what do you need to see, or what gets you comfortable sending proprietary code to other external services?
Honestly? It just has to be local.
At work, we have contracts with OpenAI, Anthropic, and Google with isolated/private hosting requirements, coupled with internal, custom, private API endpoints that enforce our enterprise constraints. Those endpoints perform extensive logging of everything, and reject calls that contain even small portions of code if it's identified as belonging to a secret/critical project.
There's just no way we're going to negotiate, pay for, and build something like that for every possible small AI tooling vendor.
And at home, I feed AI a ton of personal/private information, even when just writing software for my own use. I also give the AI relatively wide latitude to vibe-code and execute things. The level of trust I need in external services that insert themselves in that loop is very high. I'm just not going to insert a hard dependency on an external service like this -- and that's putting aside the whole "could disappear / raise prices / enshittify at any time" aspect of relying on a cloud provider.
Yeah I get the dependency concern, and also I think about the trust and pricing challenge a lot. I might be getting ahead of my skis here, but living in a future world, assuming there is a local service, what would you want to see with a context management service for your team to actually use it? Or even better - pay for it?
Personally, I have been using beads for a few days on a couple of projects. I also like https://github.com/Dicklesworthstone/beads_viewer which is a nice tui for beads (with some additional workflow i haven't tried). I have found its been useful for longer, multi-session implementations. Its easier to get back into the work. I wouldn't go so far as to it couldn't do the work without it, but so far it seems smoother. These things are hard to measure. I think the it's really not that different than how an engineering team would use jira but more hierarchical, which helps preserve context, and with prebuilt instructions for how the agent should use it.
You should never let context get that high unless you’re doing really basic things. Somewhere 40-60% is generally the time to start thinking about exits for tougher tasks. Get out in the 60s.
I've been tinkering with building something similar for myself - though for a generic chatbot, rather than for Claude (not every task is coding, and I'd like to keep !). From other comments (e.g. https://news.ycombinator.com/item?id=46428368, https://news.ycombinator.com/item?id=46427950) suggest that many others are already ahead of me. Any recs for tools, libraries, or approaches that I should learn from or adopt? In particular, I've found that - no matter how direct and clear the system prompt is - models have a tendency to respond verbally as if they've made a tool-call recording some gained-knowledge ("thanks! I'll remember that"), but to not actually return the JSON required to trigger the call by the tool.
Since you've already thought about this problem, I'd love to hear your feedback after giving this skill a try. It should speed up at least your basic need of having to trigger the LLM to store the memory. One of our colleagues has found success asking at the end of a research session what he missed, how he could improve, etc.
Thanks everyone for the comments, really, I wasn't expecting this.
Quite a few of you have mentioned that you store a lot of your working context across sessions in some md file - what are you actually storing? What data do you actually go back to and refer to as you're building?
It works great. You can put anything you want in there. Coding style, architecture guidelines, project explanation.
Anything the agent needs to know to work properly with your code base. Similar to an onboarding document.
Tools (Claude Code CLI, extensions) will pick them up hierarchically too if you want to get more specific about one subdirectory in your project.
AGENTS.md is similar for other AI agents (OpenAI Codex is one). It doesn't even have to be those - you can just @ the filename at the start of the chat and that information goes in the context.
The naming scheme just allows for it to be automatic.
The past few weeks I've been experimenting with using less context and less memory and it's been going really well. Where before I'd try to do a bunch of fairly related things in a single session, experimenting with compacting more or less frequently, now I'm clearing my context or exiting and restarting claude and codex. It seems to help it focus on the task at hand, hasn't tended to go off into the weeds as much, and my token costs have dropped way down.
Combined with a good AGENTS.md, it seems to be working really well.
That’s been my experience as well. I find I usually get better output if I create a new conversation for each thing I need. I’ve found that the only times it’s better to continue an existing conversation is if I want to have it make small improvements or changes to something it just wrote, as it tends to do better with the previous context still there. But even that only goes so far, then the scale tips and it works much better with a clean slate. I especially don’t want totally unrelated conversations polluting the context which is why I have all memory features turned off in all the web chat UI’s for the models I use.
There's a lot of people interested in forming some sort of memory layer around vendored LLM services. I don't think they realize how much impact a single error that disappears from your immediate attention can have on downstream performance. Now think of the accrual of those errors over time and your lack of ability to discern if it was service degradation or a bad prompt or a bad AGENTS.md OR now this "long term memory" or whatever. If this sort of feature will ever be viable, the service providers will offer the best solution only behind their API, optimized for their models and their infrastructure.
We're just dealing with instructions. Claude.md is handled by Claude Code. It is forgotten almost entirely often when the context fills.
Okay, what is an agent? An agent is basically a Claude.md file, but you make it extremely granular. So it only has instructions of let's say, Typescript.
We're all just doing context management here. We're trying to make sure our instructions that matter stay.
To do that, we have to remove all other instructions from the picture.
When you're doing typescript, you only know type script things.
Okay, what's a skill? A skill is doing a single thing with type script. Why? So that the context is even smaller.
Instead of the agent having every single instruction you need about typescript, you put them in skills so they only get put into context when that thing is needed.
But skills are also where you connect deterministic programs. For example, I have a skill for creating images in nano banana.
So when the Typescript Agent needs to create an image, it calls the skill, that calls the python script, to create images in nano banana.
We're managing all the context to only be available when it's needed, keeping all other instructions out.
Though I have found repo level claude.md that is updated everytime claude makes a mistake plus using —restore to select a previous relevant session works well.
There is no way for Anthropic to optimize Claude code or the underlying models for these custom setups. So it’s probably better to stick with the patterns Anthropic engineers use internally.
I mostly use it during long Claude Code research sessions so I don’t lose my place between days.
I run it in automatic mode with decent namespacing, so thoughts, notes, and whole conversations just accumulate in a structured way. As I work, it stores the session and builds small semantic, entity-based hypergraphs of what I was thinking about.
Later I’ll come back and ask things like:
what was I actually trying to fix here?
what research threads exist already?
where did my reasoning drift?
Sometimes I’ll even ask Claude to reflect on its own reasoning in a past session and point out where it was being reactive or missed connections.
Feels like this is solving a problem that /compact should solve but doesn't. The fact that post-compaction Claude 'feels dumber' suggests the summarization is too aggressive? Would be interesting if Anthropic exposed more control over what gets preserved vs. compressed ... or let users provide their own summary template.
Different approach: I continuously refine my global CLAUDE.md (~/.claude/CLAUDE.md) instead of external memory systems.
I work primarily in Python and maintain extensive coding conventions there - patterns allowed/forbidden, preferred libs, error handling, etc. Custom slash commands like `/use-recommended-python` (loads my curated libs: pendulum over datetime, httpx over requests) and `/find-reinvented-the-wheel` to catch when Claude ignored existing utilities.
My use case: multiple smaller Python projects (similar to steipete's workflow https://github.com/steipete), so cross-project consistency matters more than single-codebase context.
Yes, ~15k tokens for CLAUDE.md + rules. I sacrifice context for consistency. Worth it.
Also baked in my dev philosophy: Carmack-style - make it work first, then fast. Otherwise Claude over-optimizes prematurely.
These memory abstractions are too complicated for me and too inconsistent in practice. I'd rather maintain a living document I control and constantly refine.
You can use it with summaries for sure, but summaries often miss edge cases and long sessions drift. This makes it easier to jump between tasks, come back days later, and reorient without missing something that the summarization or compaction might have gotten rid of. I've often found post-compaction, the memory of even the current session feels so much dumber.
Each time an LLM looks at my project, it's like a newcomer has arrived. If it keeps repeating mistakes, it's because my project sucks.
It's an unique opportunity. You can have lots of repeated feedback from "infinite newcomers" to a project, each of their failures an opportunity to make things clearer. Better docs (for humans, no machine-specific hacks), better conventions, better examples, more intuitive code.
That, in my opinion, is how markdown (for machines only and not humans) will fall. There will be a breed of projects that thrives with minimal machine-specific context.
For example, if my project uses MIDI, I'm much better doing some specialized tools and examples that introduce MIDI to newcomers (machines and humans alike) than writing extensive "skill documents" that explain what MIDI is and how it works.
Think like a human do. Do you prefer being introduced to a codebase by reading lots of verbose docs or having some ready-to-run examples that can get you going right away? We humans also forget, or ignore, or keep redundant context sources away (for a good reason).
maybe you are in a claude code session and think "didn't i already make design doc for system like this one?" Or you could even look at your thought process in a previous session and reflect. but rn i mainly use it for reviewing research and the hypergraph retrieval
every time Claude code loads or it compacts the conversation it loses its context so I always type in: read CLAUDE.md .... which usually solves the problem... I run Claude code on a few screen sessions in different directories for months
I absolutely love this concept! It's like the thing that I've been looking for my whole life. Well, at least since I've been using Claude Code, which is this year.
I'm sold.
With that said, I can't think of a way that this would work. How does this work? I took a very quick glance, and it's not obvious at first glance.
The whole problem is, the AI is short on context, it has limited memory. Of course, you can store lots of memory elsewhere, but how do you solve the problem of having the AI not know what's in the memory as it goes from step to step? How does it sort of find the relevant memory at the time that that relevance is most active?
Could you just walk through the sort of conceptual mechanism of action of this thing?
Appreciate it - yeah, you're right, models don't work well when you just give it a giant dump of memory.
We store memories in a small DB - think key/value pair with embeddings
Every time you ask Claude something, the skill:
1. Embeds the current request.
2. Runs a semantic + timestamp-weighted search over your past sessions.
Returns only the top N items that look relevant to this request.
3. Those get injected into the prompt as context (like extra system/user messages), so Claude sees just enough to stay oriented without blowing context limits.
Think of it like: Attention over your historical work, more so than brute force recall. Context on demand basically giving you an infinite context window. Bookmark + semantic grep + temporal rank. It doesn’t “know everything all the time.” It just knows how to ask its own past: “What from memory might matter for this?”
When you try it, I’d love to hear where the mechanism breaks for you.
yeah so you can run it in automatic mode, or read only mode. In automatic mode it hooks onto the conversation and tool calls so you get the entire conversation stored. If you dont want to get super deep, then read only is safe and only stores what you ask. You could ask it things like "why is my reasoning dumb" by recalling passed conversations, or even give it the claude tool call sequence and ask "how can claude be smarter about next time".
I think of it like a file tree with proper namespacing and keep abstract concepts in separate directories. so like my food preferences will be in like /preferences/sandos. or you can even do things like /system-design preferences and then load them into a relevant conversation for next time.
You can work around a lot of the memory issues for large and complex tasks just by making the agent keep work logs. Critical context to keep throughout large pieces of work include decisions, conversations, investigations, plans and implementations - a normal developer should be tracking these and it's sensible to have the agent track them too in a way that survives compaction.
None, that's what I'm trying to say. My favorite is just storing project context locally in docs that agents can discover on their own or I can point to if needed. This doesn't require me to upload sensitive code or information to anonymous people's side projects and has and equivalent amount of hard evidence for efficacy (zero), but at least has my own anecdotal evidence of helping and doesn't invite additonal security risk.
People go way overboard with MCPs and armies of subagents built on wishes and unproven memory systems because no one really knows for sure how to get past the spot we all hit where the agentic project that was progressing perfectly hits a sharp downtrend in progress. Doesn't mean it's time to send our data to strangers.
All of these systems are for managing context.
You can generally tell which ones are actually doing something if they are using skills, with programs in them.
Because then, you're actually attaching some sort of feature to the system.
Otherwise, you're just feeding in different prompts and steps, which can add some value, but okay, it doesn't take much to do that.
Like adding image generation to claude code with google nano banana, a python script that does it.
That's actually adding something claude code doesn't have, instead of just saying "You are an expert in blah"
another is one claude code ships with, using rip grep.
Those are actual features. It's adding deterministic programs that the llm calls when it needs something.
So a better question to ask is - Do you have any ideas for an objective way to a measure a performance of agentic coding tools? So we can truly determine what improves performance or not.
I would hope that internal to OpenAI and Anthropic they use something similar to the harness/test cases they use for training their full models to determine if changes to claude code result in better performance.
I’m not sure where the ‘despite’ comes in. Experts and vets have opinions and this is probably the best online forum to express them. Lots of experts and vets also dislike extremely popular unrelated tools like VB, Windows, “no-code” systems, and Google web search… it’s not a personality flaw. It doesn’t automatically mean they’re right, either, but ‘expert’ and ‘vet’ are earned statuses, and that means something. We’ve seen trends come and go and empires rise and fall, and been repeatedly showered in the related hype/PR/FUD. Not reflexively embracing everything that some critical mass of other people like is totally fine.
My approach is literally just a top-level, local, git version controlled memory system with 3 commands:
- /handoff - End of session, capture into an inbox.md
- /sync - Route inbox.md to custom organised markdown files
- /engineering (or /projects, /tasks, /research) - Load context into next session
I didn't want a database or an MCP server or embeddings or auto-indexing when I can build something frictionless that works with git and markdown.
Repo: https://github.com/ossa-ma/double (just published it publicly but its about the idea imo)
Writeup: https://ossa-ma.github.io/blog/double
I will typically make multiple '/handoff's per day as I use Claude code whereas I typically use '/sync' at the end of the day to organise them all at once.
I think at this point in time, we both have it right.
Otherwise the ability to search back through history is a valuable simple git log/diff or (rip)grep/jq combo over the session directory. Simple example of mine: https://github.com/backnotprop/rg_history
I feel that way too. I have a lot of these things.
But the reality is, it doesn't really happen that often in my actual experience. Everyone is very slow as a whole to understand what these things mean, so far you get quite a bit of time just with an improved, customized system of your own.
https://backnotprop.com/blog/50-first-dates-with-mr-meeseeks...
So... it's tough. I think memory abstractions are generally a mistake, and generally not needed, however I also think that compacting has gotten so wrong recently that they are also required until Claude Code releases a version with improved compacting.
But I don't do memory abstraction like this at all. I use skills to manage plans, and the plans are the memory abstraction.
But that is more than memory. That is also about having a detailed set of things that must occur.
I think planning is a critical part of the process. I just built https://github.com/backnotprop/plannotator for a simple UX enhancement
Before planning mode I used to write plans to a folder with descriptive file names. A simple ls was a nice memory refresher for the agent.
I am working alone. So I am instead having plans automatically update. Same conception, but without a human in the mix.
But I am utilizing skills heavily here. I also have a python script which manages how the LLM calls the plans so it's all deterministic. It happens the same way every time.
That's my big push right now. Every single thing I do, I try to make as much of it as deterministic as possible.
We use Cursor where I work and I find it a good medium for still being in control and knowing what is happening with all of the changes being reviewed in an IDE. Claude feels more like a black box, and one with so many options that it's just overwhelming, yet I continue to try and figure out the best way to use it for my personal projects.
Claude code suffers from initial decision fatigue in my opinion.
Agree with the other comments: pretty much running vanilla everything and only the Playwright MCP (IMO way better than the native chrome integration) and ccstatusline (for fun). Subagents can be as simple as saying "do X task(s) with subagent(s)". Skills are just self @-ing markdown files.
Two of the most important things are 1) maintaining a short (<250 lines) CLAUDE.md and 2) having a /scratch directory where the agent can write one-off scripts to do whatever it needs to.
This helps it organize temporary things it does like debugging scripts and lets it (or me) reference/build on them later, without filling the context window. Nothing fancy, just a bit of organization that collects in a repo (Git ignored)
I've TL'd and PM'd as well as IC'd. Now my IC work feels a lot more like a cross between being a TL and being a senior with a handful of exuberant and reasonably competent juniors. Lots of reviewing, but still having to get into the weeds quickly and then get out of their way.
Things that need special settings now won’t in the future and vice versa.
It’s not worth investing a bunch of time into learning features and prompting tricks that will be obsoleted soon
They do get better, but not enough to change any of the configuration I have.
But you are correct, there is a real possibility that the time invested with be obsolete at some point.
For sure the work towards MCPs are basically obsolete via skills. These things happen.
how would that be a "skill"? just wrap the mcp in a cli?
fwiw this may be a skill issue, pun intended, but i can't seem to get claude to trigger skills, whereas it reaches for mcps more... i wonder if im missing something. I'm plenty productive in claude though.
So a Skill is just a smaller granulatrity level of that concept. It's just one of the individual things an MCP can do.
This is about context management at some level. When you need to do a single thing within that full list of potential things, you don't need the instructions about a ton of other unrelated things in the context.
So it's just not that deep. It would be having a python script or whatever that the skill calls that returns the runtime dependencies and gives them back to the LLM so they can refactor without blindly greping.
Does that make sense?
It's always interesting reading other people's approaches, because I just find them all so very different than my experience.
I need Agents, and Skills to perform well.
I agree that this level of finetuning feels overwhelming and might let yourself doubting whether you do utilize Claude to its optimum and the beauty is, that finetunging and macro usage don't interfere, when you stay in your lane.
For example I now don't use the planing agent anymore instead incorporated this process into the normal agents much to the project's advantage. Consistency is key. Anthropic did the right thing.
Codex is quite a different beast and comes from the opposite direction so to say.
I use both, Codex and Claude Opus especially, in my daily work and found them complementary not mutual exclusive. It is like two different evangelists who are on par exercising with different tools to achieve a goal, that both share.
It's also deeply interesting because it's essentially unsolved space. It's the same excitement as the beginning of the internet.
None of us know what the answers will be.
I've been trying to write blogs explaining it recently, but I don't think I'm very good at making it sound interesting to people.
What can I explain that you would be interested in?
Here was my latest attempt today.
https://vexjoy.com/posts/everything-that-can-be-deterministi...
Here is what I don't get. it's trivial to do this. Mine is of course customized to me and what I do.
The idea is to communicate the ideas, so you can use them in your own setup.
It's trivial to put for example, my do router blog post in claude code and generate one customized for you.
So what does it matter to see my exact version?
These are the type of things I don't get. If I give you my details, it's less approachable for sure.
The most approachable thing I could do would be to release individual skills.
Like I have skills for generating images with google nano banana. That would be approachable and easy.
But it doesn't communicate the why. I'm trying to communicate the why.
When you've tried 10 ways of doing it but they all end up getting into a "feed the error back into the LLM and see what it suggests next" you aren't that motivated to put that much effort into trying out an 11th.
The current state of things is extremely useful for a lot of things already.
I'm not sure if the problems you run into with using LLMs will be solved if you do it my way. My problems are solved doing it my way. If I heard more about your problems, I would have a specific answer to them.
These are the solutions to where I have run into issues.
For sure, but my solutions are not feed the error back into the LLM. My solutions are varied, but as the blog shows, they are move as much as possible into scripts, and deterministic solutions, and keep the LLM to the smallest possible scope.
The current state of things is extremely useful for a subset of things. That subset of things feels small to me. But it may be every thing a certain person wants to do exists in that subset of things.
It just depends. We're all doing radically different things, and trying very different things.
I certainly understand and appreciate your perspective.
My basic problem is: "first-run" LLM agent output frequently does one or more of the following: fails to compile/run, fails existing test coverage, or fails manual verification. The first two steps have been pretty well automated by agents: inspect output, try to fix, re-run. IME this works really well for things like Python, less-well for things like certain Rust edge cases around lifetimes and such, or goroutine coordination, which require a different sort of reasoning than "typical" procedural programming.
But let's assume that the agents get even better at figuring out the deal with the more specialized languages/features and are able to iterate w/o interaction to fix things.
If the first-pass output still has issues, I still have concerns. They aren't "I'm not going to use these tools" concerns, because I also sometimes write bugs, and they can write the vast majority of code faster than I can.
But they are "I'm not gonna vibe-code my day job" concerns because the existence of trivially-catchable issues suggests that there's likely harder-to-catch issues that will need manual review to make sure (a) test coverage is sufficient, (b) the mental model being implemented is correct, (c) the outside world is interacted with correctly. And I still find bugs in these areas that I have to fix manually.
This all adds up to "these tools save me 20-30% of my time" (the first-draft coding) vs "these agents save me 90% of my time."
So I'm kinda at a plateau for a few months where it'll be hard to convince me to try new things to try to close that 20-30% -> 90% number.
The real issue is I don’t know the issues ahead of time. So each experience is an iteration stopping things I didn’t know would happen.
Thankfully, I’m not trying to sell anyone anything. I don’t even want people to use what I use. I only want people to understand the why of what I do, and how it adds me value.
I think it’s important to understand this thing we use as best we can.
The personal value you can get, is entirely up to your tolerance for it.
I just enjoy the process
For large codebases (my own has 500k lines and my company has a few tens of millions) you need something better like RPI.
If nothing else just being able to understand code questions basically instantly should give you a large speed up, even without any fancy stuff.
In some sense, computers and digital things have now just become a part of reality, blending in by force.
But the things I am doing might not be the things you are doing.
If you want proof, I intend to release a game to the App Store and steam soon. At that point you can judge if it built a thing adequately.
I hope you're just one of the ones who figured it out early and all the hype isn't fake bullshit. I'd much rather be proven wrong than for humanity to have wasted all this time and resources.
I think of this stuff as trivial to understand from my point of view. I am trying to share that.
I have nothing to sell, I don’t want anyone to use my exact setup.
I just want to communicate the value as I see it, and be understood.
The vast majority of it all is complete bullshit, so of course I am not offended that I may sound like 1000 other people trying to get you to download my awesome Claude Code Plugins repo.
Except I’m not actually providing one lol
the docs if you are curious: https://www.ensue-network.ai/docs
Consider more when you're 50+ hours in and understand what more you want.
The PMs were right all along!
But imagine how hard it would be if these kids had short term memory only and they would not know what to focus on except what you tell them to. You literally have to tell them "Here is A-Z pay attention to 'X' only and go do your thing". Add in other managers for this party like a caterer, clowns, your spouse and they also have to tell them that and remember, communicate what other managers have done. No one has solved for this, really.
This is what it felt like in 2025 to code with LLMs on non trivial projects, with some what of an improvement as the year went by. But I am not sure much progress was made in fixing the process part of the problem.
The project is still in alpha, so you could shape what we build next - what do you need to see, or what gets you comfortable sending proprietary code to other external services?
Honestly? It just has to be local.
At work, we have contracts with OpenAI, Anthropic, and Google with isolated/private hosting requirements, coupled with internal, custom, private API endpoints that enforce our enterprise constraints. Those endpoints perform extensive logging of everything, and reject calls that contain even small portions of code if it's identified as belonging to a secret/critical project.
There's just no way we're going to negotiate, pay for, and build something like that for every possible small AI tooling vendor.
And at home, I feed AI a ton of personal/private information, even when just writing software for my own use. I also give the AI relatively wide latitude to vibe-code and execute things. The level of trust I need in external services that insert themselves in that loop is very high. I'm just not going to insert a hard dependency on an external service like this -- and that's putting aside the whole "could disappear / raise prices / enshittify at any time" aspect of relying on a cloud provider.
Just their thought management git system works pretty well for me TBH. https://www.humanlayer.dev/
I’m never stopped and Claude always remembers what we’re doing.
This pattern has been highly productive for 8 months.
Quite a few of you have mentioned that you store a lot of your working context across sessions in some md file - what are you actually storing? What data do you actually go back to and refer to as you're building?
1a directly from Anthropic on agentic coding and Claude Code best practices.
"Create CLAUDE.md files"
https://www.anthropic.com/engineering/claude-code-best-pract...
It works great. You can put anything you want in there. Coding style, architecture guidelines, project explanation.
Anything the agent needs to know to work properly with your code base. Similar to an onboarding document.
Tools (Claude Code CLI, extensions) will pick them up hierarchically too if you want to get more specific about one subdirectory in your project.
AGENTS.md is similar for other AI agents (OpenAI Codex is one). It doesn't even have to be those - you can just @ the filename at the start of the chat and that information goes in the context.
The naming scheme just allows for it to be automatic.
Combined with a good AGENTS.md, it seems to be working really well.
If you're using them though, we no longer have the problem of Claude forgetting things.
Agents are an md file with instructions.
Skills are an md file with instructions.
Commands are.. you get the point.
We're just dealing with instructions. Claude.md is handled by Claude Code. It is forgotten almost entirely often when the context fills.
Okay, what is an agent? An agent is basically a Claude.md file, but you make it extremely granular. So it only has instructions of let's say, Typescript.
We're all just doing context management here. We're trying to make sure our instructions that matter stay.
To do that, we have to remove all other instructions from the picture.
When you're doing typescript, you only know type script things.
Okay, what's a skill? A skill is doing a single thing with type script. Why? So that the context is even smaller.
Instead of the agent having every single instruction you need about typescript, you put them in skills so they only get put into context when that thing is needed.
But skills are also where you connect deterministic programs. For example, I have a skill for creating images in nano banana.
So when the Typescript Agent needs to create an image, it calls the skill, that calls the python script, to create images in nano banana.
We're managing all the context to only be available when it's needed, keeping all other instructions out.
Does that help?
Very clearly AI written
Though I have found repo level claude.md that is updated everytime claude makes a mistake plus using —restore to select a previous relevant session works well.
There is no way for Anthropic to optimize Claude code or the underlying models for these custom setups. So it’s probably better to stick with the patterns Anthropic engineers use internally.
And also - I genuinely worry about vendor lock-in, do you?
I run it in automatic mode with decent namespacing, so thoughts, notes, and whole conversations just accumulate in a structured way. As I work, it stores the session and builds small semantic, entity-based hypergraphs of what I was thinking about.
Later I’ll come back and ask things like:
what was I actually trying to fix here?
what research threads exist already?
where did my reasoning drift?
Sometimes I’ll even ask Claude to reflect on its own reasoning in a past session and point out where it was being reactive or missed connections.
Claude itself can just update the claude.md file with whatever you might have forgot to put in there.
You can stick it in git and it lives with the project.
https://github.com/thedotmack/claude-mem
I’ll give this a go though and let you know!
https://github.com/backnotprop/rg_history
I work primarily in Python and maintain extensive coding conventions there - patterns allowed/forbidden, preferred libs, error handling, etc. Custom slash commands like `/use-recommended-python` (loads my curated libs: pendulum over datetime, httpx over requests) and `/find-reinvented-the-wheel` to catch when Claude ignored existing utilities.
My use case: multiple smaller Python projects (similar to steipete's workflow https://github.com/steipete), so cross-project consistency matters more than single-codebase context.
Yes, ~15k tokens for CLAUDE.md + rules. I sacrifice context for consistency. Worth it.
Also baked in my dev philosophy: Carmack-style - make it work first, then fast. Otherwise Claude over-optimizes prematurely.
These memory abstractions are too complicated for me and too inconsistent in practice. I'd rather maintain a living document I control and constantly refine.
Or, over continuing the same session and compacting?
Each time an LLM looks at my project, it's like a newcomer has arrived. If it keeps repeating mistakes, it's because my project sucks.
It's an unique opportunity. You can have lots of repeated feedback from "infinite newcomers" to a project, each of their failures an opportunity to make things clearer. Better docs (for humans, no machine-specific hacks), better conventions, better examples, more intuitive code.
That, in my opinion, is how markdown (for machines only and not humans) will fall. There will be a breed of projects that thrives with minimal machine-specific context.
For example, if my project uses MIDI, I'm much better doing some specialized tools and examples that introduce MIDI to newcomers (machines and humans alike) than writing extensive "skill documents" that explain what MIDI is and how it works.
Think like a human do. Do you prefer being introduced to a codebase by reading lots of verbose docs or having some ready-to-run examples that can get you going right away? We humans also forget, or ignore, or keep redundant context sources away (for a good reason).
https://github.com/jMyles/memory-lane
Why did you need to use AI to write this post?
I'm sold.
With that said, I can't think of a way that this would work. How does this work? I took a very quick glance, and it's not obvious at first glance.
The whole problem is, the AI is short on context, it has limited memory. Of course, you can store lots of memory elsewhere, but how do you solve the problem of having the AI not know what's in the memory as it goes from step to step? How does it sort of find the relevant memory at the time that that relevance is most active?
Could you just walk through the sort of conceptual mechanism of action of this thing?
1. Embeds the current request.
2. Runs a semantic + timestamp-weighted search over your past sessions. Returns only the top N items that look relevant to this request.
3. Those get injected into the prompt as context (like extra system/user messages), so Claude sees just enough to stay oriented without blowing context limits.
Think of it like: Attention over your historical work, more so than brute force recall. Context on demand basically giving you an infinite context window. Bookmark + semantic grep + temporal rank. It doesn’t “know everything all the time.” It just knows how to ask its own past: “What from memory might matter for this?”
When you try it, I’d love to hear where the mechanism breaks for you.
Then Claude uses the MCP tools according to the SKILL definition: https://github.com/mutable-state-inc/ensue-skill/blob/main/s...
I think of it like a file tree with proper namespacing and keep abstract concepts in separate directories. so like my food preferences will be in like /preferences/sandos. or you can even do things like /system-design preferences and then load them into a relevant conversation for next time.
Text Index of past conversations, using prompt-like summaries.
https://news.ycombinator.com/item?id=46427016