141–150 of 206 posts
TIL the prompt cache lasts 1 hour. I thought it was reduced to 5 minutes.
1. My agent writes code.
2. Then it creates tests and verifies that all of them actually work, not just pass. To do this, my agent writes the test, then it deletes the code it covers, reruns the test, confirms it goes red, and finally puts the code back.
3. I receive the ready-to-test code and environment setup.
4. I check that the business logic works as I expected it to be on a working product. Here we usually do several iterations of coding and bug fixing.
5. When the manual part is finished, the agent starts an external review using /code review skill. At that stage, it makes some additional fixes and corrections to the tests.
6. Finally, a branch is ready to be merged. We start CI/CD and wait until the run finishes successfully.
That's what I actually use because it generally works.
Note about only docs PRs: I just ask the agent to make the changes, then it runs the / code review skill, and then we merge the branch into main without CI running.
What I see is that I have to read a bunch of stuff and go through a bunch of hassle to save money when the root of it is that if I tell an AI to do work on a task while I'm busy with something else and come back later I've doubled my cost because the cache expires too quickly?
It's all so tiring. I care less and less about Claude every single day because of the usage caps and the constant optimizing that has to be done. The whole point of AI was to get past this type of bullshit. They've failed miserably at their jobs.
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You can generally understand your AWS cloud usage, and waste can be self evident with their existing tools. Not at all with llms. A postgres index post is unlikely to reach front page. It's already part if the docs, and should include more context to be read worthy. They are not equal comparison. This before the fact that there is no guarantee that a model follows your agent instructions (plenty of easy to reach for…
I thought that software engineers were supposed to do, you know, engineering - solving hard problems, dealing with uncertainty. But you might be right, engineering around the difficult LLM primitive might be a task which is just too hard for your typical software engineer, as you said, they want predictability, hand holding, determinism, most are unable to deal with the real world which is not a spherical cow in a va…
When AI firms ate more than half of global VC private investment in 2025 https://www.oecd.org/en/about/news/announcements/2026/02/ai-... I would expect better results than what we have today.
The most well paid people in the industry brought us here. And "here" is very much as fuzzy as last year with better harnessing towards the local optima. And I say local optima because even the perceived capabilities have slowed down, nevermind the benchmark numbers which are in aggrement.
The best paid engineers in the world, with almost no practical budget limit, still deliver shoddy quality software with AI. Is that not fact? And if it is what does that say for the rest of us.
You are allowed to believe. I'm still waiting for the beneficial results, not only those that benefit griefters, hackers and scammers. AI has been a huge boon there.
Reality will materialize and markets will redress hopefully once they go public. Which they very much seem to be hesitant to do right now.
Recently I came across the /handoff skill, which I've been using a lot. I find it much better than /compact. Basically: - /handoff file creates a short document with the important context from your current session and maybe next steps as checklist. - You can then start a fresh session with /continue file - You can also hand the work from Claude to ChatGPT, or the other way around. Very useful at time of session limit…
I've been asking Claude to remember important points from our session, or future tasks. Then /clear and continue.
Recently I came across the /handoff skill, which I've been using a lot. I find it much better than /compact. Basically: - /handoff file creates a short document with the important context from your current session and maybe next steps as checklist. - You can then start a fresh session with /continue file - You can also hand the work from Claude to ChatGPT, or the other way around. Very useful at time of session limit…
- review system prompt + cut it down or remove completely
- review agents.md file(s), check which ones are loaded, remove or improve them
- review context spam from tools, skills etc, de-activate all, see what needs re-adding
- review past sessions to see where tokens get wasted
- keep sessions short (be conscious about compaction)
- form a habit of starting new sessions
- deliberately practice how to effectively get the right context into a new session (vs hanging on to a 'good' session)
- you can ask the agent to write the essential context into a .md file and have the new session read that
- learn about forking sessions
- experiment with starting sessions from a custom-built history/context
that said: you will surprised by how well current models can navigate (way better than last year!)
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I thought that software engineers were supposed to do, you know, engineering - solving hard problems, dealing with uncertainty. But you might be right, engineering around the difficult LLM primitive might be a task which is just too hard for your typical software engineer, as you said, they want predictability, hand holding, determinism, most are unable to deal with the real world which is not a spherical cow in a va…
I'm a pleb developer, don't have the smarts, the prestige or the salary of those working at BigTech. When AI firms ate more than half of global VC private investment in 2025 https://www.oecd.org/en/about/news/announcements/2026/02/ai-... I would expect better results than what we have today. The most well paid people in the industry brought us here. And "here" is very much as fuzzy as last year with better harnessing…
Not sure what your baseline was, if you said 10 years ago "in 2026 you'll be able to describe an app into the microphone, and the computer will write by itself in one day 50k lines of code to implement it, in a language and tech stack of your choosing, costing $200, and it will sort-of-work, and it will be at least as good as a junior-level programmer writing it from the same requirements in 3 months", most people would have said "implausible, that's at least 50 years away"
Recently I came across the /handoff skill, which I've been using a lot. I find it much better than /compact. Basically: - /handoff file creates a short document with the important context from your current session and maybe next steps as checklist. - You can then start a fresh session with /continue file - You can also hand the work from Claude to ChatGPT, or the other way around. Very useful at time of session limit…
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> Plus your handoff files becomes a useful piece of project memory that you can reference later. I've been doing this since I started agentic development, and have a whole framework based on this; Simply put I define workflow s that output templated files for everytype of tasks that happens in development. It's a powerful pattern I'd recommend everyone.
Can you please add more details? Thank you in advance!
- Prefix-numbered sets of documents, keeping them clustered visually and easily referenced by humans and agents. (124.5 = docset 125, phase 5) - Each doc gets a suffix. Most of the time STRATEGY is the first doc. Sometimes NOTES, DISCUSSION, or HANDOFF though. - Once the strategy doc is comprehensive, the multi-phase checklist PLAN doc can emerge. - During execution, the agent drives the PLAN while purposefully expanding lightly scaffolded phases before entry. - As the user, I drive a PROGRESS doc during the PLAN execution. This tracks the progression of my own questions and the important work summaries that I need for tracking the current work trajectory and for historic analysis over past decision-making. - Any concern that exceed the scope of the active docset numbers calls for a new one. - A "docs/archive" folder is kept where I sweep docsets into numbered eras.
I have aliases and templates too, but DDD is so simple that it's overkill.
Any modern reasoning agent can take the plain explanation above and understand you effortlessly when you say "open a new docset strategy" or "read docset 125 and proceed".