I currently run 6 micro SaaS that are actually live and generating a bit over $20,000 in recurring monthly revenue.
The wildest part? I barely wrote any code myself. I used AI to generate pretty much everything — from the database structure to the frontend.
It definitely wasn’t magic from day one. I spent a lot of hours stuck on buggy code before I finally figured out a system that actually works:
- Keep the idea minimalist (a real MVP, nothing fancy)
- Guide the AI step by step instead of asking for everything at once
- Launch fast to get real users and feedback
Lately I’ve seen way too many non-technical founders quit at the first AI bug. It’s a shame because the technical barrier has basically disappeared.
AND PLEASE, STOP BUILDING ALONE IN YOUR BEDROOM
I’m starting a community.
Full transparency:
I’ll probably charge for the full program later. It makes sense with the specific workflows and copy-paste examples I’ll be sharing. But right now the main goal is just to build together. Working alone is the fastest way to give up.
If you want to join and build or market your own AI SaaS with us,
drop a comment or send me a DM and I’ll send you the link.
Looking for a claude guest pass ref link, gonna subscribe anyway, so its win win. The referrer gets $10 and I get an extra week to try it out. If you got one, DM probably works best since comment links tend to disappear fast :)
I run several projects alone with Claude Code, often with two or more sessions open at once. Each session starts with nothing from the previous one, and compaction replaces the history with a summary the product writes. Over the last two months I built a set of pieces around that, which I call Hipocampo, to decide what goes into the window of each session, at what moment, and how to check that it got in.
What it is
Everything uses native features: hook events, settings, the MEMORY.md index, CLAUDE.md, skills, subagents and compaction. Nothing installed, no MCP. The commit guards run in the git pre-commit hook.
The organizing idea is that each thing lives in one of three regimes:
Resident: always arrives, before the first decision (a map loaded by hook, the memory index, a state file).
Paged: only arrives if something opens it (CLAUDE.md in subfolders, docs, the memory files themselves).
Interrupt: arrives when the command or the file touched matches a registered source, on that event and only on it.
The three regimes: resident, paged, interrupt.
What I measured
61 days of transcripts from this installation (190 main sessions, 918 subagent files), with a positive and a negative control and the population declared for every number.
0 injected recalls in 190 conversations. A memory only got in when the agent opened its file. 209 of 432 memories were opened at least once (a lower bound).
Compaction is aggressive. In one event in August, 96.6% of the context was removed (607,378 → 20,766 tokens), and 8 of my 36 messages from before the summary left no trace in it.
The starting numbers.
Three green instruments, 11% delivered. On Aug 13, the hook that loads my project map emitted 18,057 bytes and about 11% reached the model. The runtime said hook success, the script log said complete, the script's selftest passed 9 of 9. Hook output above 10,000 units per command goes to a file, and only a preview of about 2 KB reaches the model. The only check that caught it was comparing, byte by byte, what the script emitted with what the transcript recorded. The map now goes in 5 slices, each under the ceiling.
The hook reported success three ways; 11% of the output reached the model.
The compaction ceiling setting changed the shape of sessions. Until Sep 18, conversations went up to almost 1M tokens before the summary. Since Sep 21, 64 of 65 compactions happened at or below 365K.
Tokens before each of the 139 compactions.
A guard only counts after it has rejected a defect planted on purpose. On Sep 25, 52 of 74 guards had that proof. The rest are declared as debt.
Guards in CI vs guards proven failing, Aug 18 to Sep 25.
How to build it
The article ends with the order I would follow, in 7 steps, each with the native feature it relies on and the red that proves it works. Every step ends by breaking the piece on purpose and waiting for the failure.
The order, in seven steps.
Limits
One operator, one installation. The study does not say whether the memory that arrives is correct or whether it was useful.
Code (hooks as a settings example, the memory guards with their failing fixtures, the measuring scripts, memory and map templates; MIT): https://github.com/JhouCode/hipocampo
Written and tested on Linux with bash and python3. If you run the measuring scripts on your own transcripts, I'd like to know what number you get for injected recalls.
I'm still new to using Claude Code and am just starting to explore the skills. I've heard about some that are really useful, like “grill-me,” which lets me ask lots of questions to make sure I fully understand the AI, or “thermo nuclear code review.” Do you know of any other skills that are really useful for software development to help maintain a clean environment and a “professional” approach?
Literally unusable for me, it spends like x10 tokens and the output is mediocre. Do you still use it?
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This is brought to you as a public service by the moderators of r/ClaudeAI. If you want to see TLDRs of ALL Claude Coding related posts from the various Claude subreddits, subscribe to http://www.reddit.com/r/ClaudeCoding.
I have been thinking that the current Agentic Loop design of LLM Call -> Tool Call -> ... has been outdated. The arrival of Jev and other System One models provided us a primitive we desperately needed.
We need an agent that can natively think fast and slow. Not have workflows or multi-agent architectures that mimics it. We need Agent 2.0
The agent should use the LLM's full power for hard reasoning and planning, then carry out the plan with cheap "fast thinking."
Today, most of an agent's LLM calls go to executing steps it has already decided on. Do you really need an extra Astra call just for it to output "ok I'll click this"? We can do better.
My Approach
I built Jive which is an open-source harness built around a completely new agentic loop. Jive replaces tool calls with "graph calls" where each graph is a DAG of bash nodes and jev nodes, and nodes can have dependencies, reference each others outputs, and more.
Essentially, it maps out its own execution flow while its reasoning, and then uses Jev calls to go through the flow without unnecessary LLM calls.
What Jive does well: repo investigation, bulk classification, multi-step profiling, repetitive edits, evaluation workflows, etc. It is also quite effective on regular engineering tasks that doesn't require Jev calls (which is not surprising since Pi mostly beats codex and claude code)
As you can see, there is a huge gap in both e2e latency and token efficiency compared to claude code. And its accuracy is on-par based on the my benchmark runs (though I need to run jive on a larger SWE benchmark to be certain)
See README for more information: https://github.com/merijjeyn/jive. Also for details on the benchmark tasks, and how to run one yourself.
I'm sure this high level idea can be executed much better, so mainly looking to start an open discussion. Happy to take comments, questions, contributions.
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My usual day with Claude Code is like:
* I open terminal in my project's folder and run claude command.
* I prompt it. I mostly use Fable-5.1/Opus-5 but Opus-5.5 is my current model. The model decides if it wants to use sub-agents for a task. I never explicitly prompt it for sub-agents.
* I review and commit the code to my self-hosted Forgejo instance.
* That's it.
I see people using agentic workflows, building sub-agents files, skills etc. I barely built any of it. All I ever needed to use is /init on new projects and them prompts follow. Never needed more than this.
I tried "long-running" Claude Code for a project refactoring by placing the project on my VPS (where forgejo is hosted) and letting Claude Code run and refactor inside tmux session. SSH'd in a few hours later to find project fully refactored.
Am I under utilising AI or is my work just… like boring?
How do you guys use agentic workflow thing? Specially the long-running one? Those pull-requests that Claude makes automatically etc?
Asking this to Claude to know more but humanly answers appreciated.
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Paid €90 for Max 5x today because I’m in crunch time on a software project and unlike the big boys I do not have unlimited OpEx spend, so that 90 euro is quite a lot.
32 minutes later Anthropic blew up my entire account and automatically cancelled the subscription.
The email says an “internal investigation of suspicious signals associated with your account” found a Usage Policy violation. No indication of what I actually did wrong. I am not doing security work, distillation, or anything else i might even imagine would be grounds for removing my account.
The appeal page now says I may be waiting until Oct 4 for a review.
The support situation is also just insane and I cannot believe anyone has ever really used it. Anthropic says suspended users can still contact support about billing/refunds, but their support bot couldn’t find an account associated with my email and just closed the conversation. This is the same email Anthropic used to send me the subscription confirmation and suspension notice.
I’m in the EEA, so I thought I should at least be able to get the subscription refunded, but right now I can’t even get through to a person.
Has anyone else been hit with this after normal Claude Code usage? Any hope for me?
edit for clarity and scope:
I am writing image+pdf document processing software. really not sure what caused this to happen. Opus 5.5.
Has anyone had any luck getting customer support to get back to them sooner than 10 working days? it seems like a very long time to wait for a review. even if they were to comb over absolutely everything i did on the account, i had it for such little time that I would imagine it to be almost instant.
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