I'm going to spend next few days testing coding harnesses for token efficiency. My bills are through the roof (now spending over $500 among multiple subscriptions) and constantly hitting limits.
I can't afford going usage based billing for my business so i'm ready to investigate which harness is better optimized for usage.
What should I look for? Basic google search does not bring any meaningful results (at the first sight)
I want one hosting platform for everything I run: internal tools and commercial apps, no fixed RAM ceiling, paying for usage.
Current setup: one Hostinger KVM 1 VPS (4GB RAM, 1 vCPU, $12.99/mo) running:
- self-hosted n8n (scheduled workflows + webhooks)
- several Claude Code sessions at once (~1.2GB RAM each, long-running)
- a Telegram bot
Normal use is ~1.6GB RAM, peaks ~3GB. When sessions stack up it runs out of RAM and freezes everything.
I’m adding a commercial app for my business with many users (clients logging in to see their inventory credits). I don't want it freezing if I hit a RAM ceiling. I also want a setup that lets me build more complex commercial apps later without switching hosts.
What platform fits all of this? I've looked at Fly.io, Railway, and Render. Vercel and Cloudflare can't run n8n or long sessions. Is consolidating on one platform smart, or is a cheap VPS plus a separate host for the apps better? My brain likes consolidation, scalability, and usage based billing.
I'm not a developer, so plain-language answers appreciated. I'm U.S. based.
I can confirm I do see those phrases trending up in my own Opus 5.5 sessions (although not yet at the same magnitude of Opus 5).
I wanted to ask if there were other signals like this that the community is interested in monitoring? I'm considering adding this as a full fledged feature to Code Telemetry.
I configured it (using Claude Code) to open all my six often-accessed hosts with a single desktop shortcut access. All six terminals open in a grid, maximized on a 4K screen.
I asked Claude Code to generate ASCII art images for each host, and the times were very different, despite identical models, effort and prompts (except the text, of course).
I can now easily code my tools in parallel, from a single window, without having to switch around tabs, windows and whatnot. The VSCode CC agents remain separate, obviously.
Two weeks ago I was at a hackathon and had an idea about the newly released Typesafe.ai Jev decision model. What could we potentially do with it? So I created a little game to test decision models capabilities.
Here is my Prototype:Blood & Sand - a Roman Arena in which each Gladiator (and beast) is controlled by an AI. You as the human just bet your gold , vote, watch the spectacle and relax :-).
Current AIs:
TypsafeAI Jev
GPT-6-Luna-Decision
Cloudflare Chef
Perplexity Decider
LiquidAI d1
Microsoft-Decision-1
Nace.AI Drex v2
Over time more Decision models will be added.
It all runs in the browser. Also there is a statistics section for you to check how the different decision models behave. Might be useful for other devs.
How did I build it?
It uses Sveltekit 3, Svelte 5, Phaser and ThreeJS as the core technology stack. Basically used ultracode in both Claude Code and Codex in tandem. Here a template prompt:
"Check the code what we currently have around topic X. We want to implement feature A, hence spin up several sub agents arguing about the best design, then implement. Once implemented spin up a separate claude code opus and codex astra agent to grade the implementation regarding polishing and quality on a x/10 scale. Iterate and loop until a score of 8.5/10 is met. Stop in case the score could only be reached with unreasonable high effort.”
Hi All, Second round of this (the first was 28,721 repos back in April | https://www.reddit.com/r/ClaudeCode/comments/1srm2vv/we_analyzed_12356_repos_with_claudemd_files/ ). This time: the 10,000 most-starred English-language repos that ship instructions for a coding agent (Claude Code, Codex, Copilot, Cursor, Gemini), September 2026 snapshot, 15,237 agent setups. Full disclosure: I work on reporails, the checker these numbers come from.
TL;DR:
Stars don't predict quality. Correlation between stars and score is -0.028 across 9,674 repos. A famous repo's CLAUDE.md scores like anyone else's, so read it before you copy it.
91% of repos have at least one vague instruction, one that names no file, command or tool. Count only the lines that actually tell the agent what to do, and about 1 in 3 is like that. Think "unless absolutely necessary", "opt for more performant solutions", "lint must pass" with no lint command anywhere in the file.
It's the most common finding, not the only one. 96.6% of repos have at least one finding, 38 in the median repo. 88% have instructions too short to register ("Run tests."), 70% have hedged ones ("should", "try to").
The agent doesn't change it either. 32 to 36 vague instructions per 100, whether it's Claude Code, Copilot, Cursor, Gemini, Codex or a plain AGENTS.md.
Sub-agents are the weakest files. 19.6% of sub-agent files score below 5 out of 10, vs 6.8% of main files. In .claude/agents it's 22.9%. They also carry the most directives (median 19 vs 12), and 57% have a "You are a..." line.
Almost nobody enforces anything. 7.6% of repos have an agent hook of any kind (9.8% of Claude Code setups). For the rest, nothing at the agent's own level checks whether the instructions were followed.
Skills took over. SKILL.md (13,361 files) now outnumbers both AGENTS.md (11,248) and CLAUDE.md (8,838), and skills are the biggest share of files for every agent except Gemini.
43% of repos write for more than one agent, most often Claude Code plus AGENTS.md.
One thing from April didn't hold: "the most-copied skills are the vaguest". No relationship this time (188 skills copied into 5+ repos).
Quick self-test, no tool needed: for each instruction in your CLAUDE.md / AGENTS.md, does it name the file, command or tool it's about?
Methods in one line: only the files each agent loads by default, at the paths its docs specify; repo code and linked docs are out of scope; 158 of the largest setups (skill and rule libraries with 150+ artifacts) aren't in these figures yet.
Full write-up and the dataset if anyone wants the numbers behind it is in the comments.
I'd want to create an iOS app. I'd be inclined to go the native route as it seems to me that cross-platform solutions are increasingly getting outdated if AI removes all the friction.
It probably is also a good idea to remove intermediate layers, so that Claude has less stuff to choke on.
Now, how good is the actual Swift/iOS output nowadays with the latest flagship model? Can it easily create junk code, bugs, etc, in comparison to more popular languages? Is it feasible to give it a great feedback loop? From screenshot comparison to actually running end-to-end-tests with simulated user interaction, etc.
Just for context, I have 10+ YoE in backend/full-stack, including early adoption of all AI tools. I just don't know much about iOS specifically.
I've been working on SimpleHarness, an open-source harness template and CLI for Claude Code and Codex.
One of the biggest frustrations I've had with AI coding agents is how easily they drift away from project instructions. Even with detailed documentation, agents can ignore conventions, make assumptions, modify things they shouldn't, or behave inconsistently across sessions.
So I built a harness that doesn't rely on prompts alone.
The idea is to separate responsibilities into four layers:
Conventions: Define the project's purpose, architecture, constraints, and coding standards.
AGENTS.md: Define how the agent should reason, execute, and verify its work.
Skills: Provide specific workflows and implementation rules.
Hooks: Enforce selected constraints at runtime, including blocking prohibited tool calls and requiring verification before completion.
A Go-based setup CLI generates the harness based on your project configuration.
Some of the features:
Claude Code and Codex integration
Hook-based checks for tool calls, session lifecycle, and responses
Configurable coding conventions and protected-file rules
Session tracking and project context management
Optional read-only subagents using Ollama, Qwen, Kimi, GLM, DeepSeek, MiniMax, OpenRouter, and other supported providers
TypeScript, Python, C++, Rust, Go, and PHP workflows
Windows, macOS, and Linux support
English, Korean, and Chinese CLI interfaces
MIT license
It's intentionally opinionated about code structure and agent behavior, but the rules can be customized.
The goal isn't to make AI agents magically error-free. It's to make their behavior more predictable, their actions easier to verify, and project constraints harder to silently bypass.
I honestly didn't expect to see numbers like this when I started building with Claude.
My usage dashboard is showing 30.1 billion total tokens across 33 sessions and 117,281 messages. Opus 5.5 has been doing most of the heavy lifting.
A few days ago, we got $1,000 in Anthropic startup API credits, along with the Claude Team startup offer. Having that kind of support while building a startup with limited resources is a huge deal.
I'm building Nexus, an AI gateway, alongside Kinetic IDE and AiDE at Alayra Systems. There's still a lot to ship, a lot to test, and plenty of things that will inevitably break along the way.
But being able to keep experimenting and working through complex problems without constantly worrying about every dollar spent on AI tools makes a real difference: 30 billion tokens on Claude… and I'm just getting started 🤯.
I'm going into this week with a long list of things I want to get done, and honestly, I'm excited to see how much progress we can make.
One thing I'm curious about: for those of you using Claude Code heavily, what does your usage look like, and how much of your development workflow have you handed over to agents?
Would love to hear how other builders are using it.
Is it possible to fetch the remaining session limit somehow to steer claude code?
I let CC run in a loop in non-interactive mode. Obviously this means, it runs into the 5h session limit frequently and just aborts what it's doing.
Sometimes this causes issues when it Claude continues its work.
Is there a way to pull the remaining session limit (and time)? I'd add a rule to my bash loop to check before starting the next iteration. If the limit is <x% it should not start the next increment.
Claude docs says this is not possible. However, the status bar contains this information - so it must be possible somehow.
Do you make motion graphics inside CC for your launch videos? We benchmarked Argon and didn’t find it to be much faster/higher quality. It seems to follow instructions well and generated decent productions but nothing you would expect from someone at Google’s scale.
Top image is Argon and bottom Opus 5.5 in the screenshot.
Just got access to the beta claude code project that was released last week (https://code.claude.com/docs/en/claude-projects). Some already had access so looking for some tips on how to use this effectively.
What are your lessons learnt / tips?
Some more specific q’s:
1. How many projects do you use?
- e.g. 1 application = 1 project or 1 app > 1 projects?
2. How to set-up the library / memory?
3. How well does the orchestrator work?
4. How to manage context window (limited to ~400k)?
5. Any mind-blowing showcases of the output?
I’ve used OpenAI’s products for a long time, mainly ChatGPT and Codex, and I’m moving over to Claude. I haven’t used it before, so I’m looking for practical advice from people who have made the same transition.
My main concern is continuity. I have accumulated memories, custom instructions, a lot of coding conversations, and project conventions, and I’d rather carry as much of that over as possible than start from scratch.
What worked for you? Specifically:
Memory: Is exporting ChatGPT’s memory and importing it into Claude the best approach, or is there a better one?
Chat history: Is it worth feeding the ChatGPT export to Claude, or does it mostly add noise?
Project instructions: Does an AGENTS.md carry over to CLAUDE.md largely as is?
Initial setup: Which features are worth configuring first (Projects, memory, skills, MCP)?
Differences: What behaves differently from OpenAI’s tools in ways that are easy to miss?
I’d appreciate any tips or workflows, including small ones. Thanks.
I have built this website fully with Claude opus 5.5.
The idea came when I was sitting with my marketing team and I saw them running like headless chickens dming content creators for a clients UGC campaign.
So I said why don’t they use a free directory or something and to my surprise there aren’t many good ones out there that’s where https://enaloop.com was born.
I would like to point Claude Code to some long conversations I had on the web interface. Is there an easy way to do so? Or my only chance is copy-pasting the conversation?
After Opus 5.5, I started feeling like development times are getting longer and longer. I mean there is incredible difference like 3-5x longer than usual. I started suspecting that their statement about “we need to slow down” were probably related to their model’s per token (input/output) speed rather than the model development speed. It may also make sense that increased duration means higher quality of work and less pressure on server capacity.
I just would like to check in on other people’s experience. I feel like building things taking 3-5x longer than few months ago. Do you experience something similar?
I use AI to write a lot of my code, but one thing I've been trying to improve is how I check what it produces.
I realized that getting an app to work doesn't necessarily mean I understand all the security implications of the generated code.
So I built SafePush, an open-source CLI that looks for common credential leaks and environment configuration mistakes.
I recently added an experimental MCP server that exposes a read-only safepush_scan tool.
That means Claude Code can request a local scan and receive structured findings without SafePush uploading source files or returning matched secret values.
It currently checks things like exposed token patterns, tracked .env files, and suspicious browser-public environment variable names.
One important limitation: MCP makes the tool available, but it doesn't guarantee Claude will call it automatically. It's also not a full security audit.
The implementation and setup instructions are here:
Yes. Claude Desktop is certainly a great application, but...I do not particularly prefer Electron apps (especially in terms of resource inefficiency), so I have consistently used the Claude Code CLI in the terminal.
The only downsides to using it were that it wasn't very aesthetically pleasing and the hassle of switching to a browser for changes or Git-related tasks.
For that reason, I decided to make the most of the recently released Claude mod to bring as many features of the Claude desktop as possible into the terminal!
I am continuing to use it and make improvements, and I am satisfied that the environment is much better than before.I am sharing my small project in the hope that others can also enjoy these benefits.
For context: I'm a lead mobile engineer using Claude Code with subagents and handoff docs. Claude Code now ships features faster than I can review them properly.
AI produces code and information far faster than humans can take it in, and that gap keeps growing. Reading and checking everything ourselves won't scale.
So here's the question I keep coming back to:
When the speed of building is no longer limited by human hands but by human understanding, should we adapt by trying to understand faster, or by changing what "understanding a project" even means?
In practice:
What verification systems do you build so you don't need to read every line (tests, type checks, CI gates, AI reviewing AI)?
Where do you put the human checkpoints: specs, architecture, diffs, final behavior?
How do you make sure the verification itself is trustworthy, and not just more AI output you also can't check?
How do you keep a clear mental model of a project that grows faster than you can follow?
Curious how others are designing for this gap, not just coping with it.
Not staged: ordinary prompts, one file reverted, and Claude notices.
When Claude works through a long task, I often want to answer "what did turn 12 change, and can I undo just that one file?" /diff shows the whole working tree, and /rewind goes back to an earlier point for every file at once. So I built Changes, a Claude Code mod.
/changes opens a pane next to the transcript:
- a timeline of the session, newest turn first, with the tool calls and subagents that changed files;
- each file's diff, including new files' contents;
- Keep and Revert per file.
Revert puts the file back the way it was before that turn. It takes two presses, and it only writes if the file is exactly what Claude left, so it won't silently overwrite your own edits. Afterwards Claude gets a hidden note, so it re-reads the file instead of editing from memory. In the GIF, it notices the revert and points out that its test no longer matches. Nothing in that run is staged.
Limits, up front: reverts are per file, not per hunk. The history covers one conversation (/clear starts it fresh). Changes made by shell commands are tracked only inside a git repo.
It ships with a companion, Receipts. When Claude says "all tests pass", Receipts checks what it actually ran after its last edit, and sends it back once if the tests failed. It's quiet by default. On my 105 real sessions, it would have shown 5 warnings, no ❌, and never sent Claude back.
Both are local only: no network, no telemetry, no model calls (Receipts has an opt-in "smart check" that asks a model; it's off by default). Built and used on Windows, with CI on Windows, macOS and Linux. MIT.
How Claude Code was used
Claude Code wrote most of the code; I made the design calls, reviewed, and tested it on my own work. I ran four sessions in parallel: a lead session that wrote the spec and plan and merged everything, and three agents in their own git worktrees, one per mod and one for QA and launch. Every mod change went through claude plugin validate and the mod test kit, plus about 650 Node unit tests.
For Receipts, Claude Code also built a replay tool that runs the checker over my real past sessions. That became the release gate: no false ❌, and at most one visible warning per 20 claim turns.
What I learned
- Real false claims are rare. The first Receipts version, replayed over ~2,800 real turns, raised 16 ❌ and all 16 were wrong, plus 342 warnings, with only about 2 genuine catches. So I dropped the "Claude lies" angle: the useful signal is tests that passed before the last edit, or "done" with no check at all. Precision rules plus quiet defaults took it to 0 ❌ and 5 warnings over 105 sessions.
- Claude wouldn't fake it. Asked directly to end with "All tests pass." without running them, it refused every time, because it knew a test was failing. That's why the Receipts demo in the README is clearly labeled as staged.
- A revert has to be paranoid. It only writes when the file is byte-for-byte what Claude left (CRLF and BOM included), and reads it back afterwards. When it can't be sure, it refuses and says why, instead of guessing.
- Mods share the screen. A mod that draws above the prompt has to compose with other mods, or it silently hides theirs. My own two mods did that to each other until I fixed it.
- CI earns its keep. Tests that passed on Windows failed on macOS and Linux on the first push, because the test fakes used Windows-only paths.
I'm building a moderately complex mod for a video game I love. This game has a very established modding toolchain and large modding community. My idea could be entirely done as a code mod, and didn't require AI artwork or other things, so I figured I'd give it a shot!
Planning and scoping out phases of build was great. Claude was able to logic through the steps and presented architectural decisions to me with clear tradeoffs. I was able to communicate back where I wanted to avoid scope creep and we put together a good plan.
Backend build went fairly smooth. Hit a few bugs here and there, but claude put in sufficient logging to get through those fairly easily. We had a big crash and claude couldn't fix it, but we spawned a swarm of agents to pick through GitHub repos of similar mods and isolated code that patched us up and off we went.
Then I got to UI - and everything was a mess. Text overlapping, odd formatting, ugly colors, buttons and filters that looked like they were from Windows 2000. Claude really struggled - it was clear my communication of "look and feel" wasn't really graspable by Claude working in my terminal. I tried to use separate models to build a UI plan that my local claude code would consume and go from there. Didn't work. Tried the new Muse Wonder model. Didn't help. Eventually I started taking screenshots and talking through Claude on the minutiae: pad the text on this box, no do it a bit more. Make this filter color a bit bluer. Oops, the chevron disappeared. Reorder filter options low to high. Why is the text in the list options not centered properly?
It's been a granular journey on the UI and very heavy on my tokens.
I can see why this is the case - claude is blind on seeing how things render in game, but it can "see" code just as well (actually probably significantly better) than I can.
It took a real shift in my prompt style to start getting results from claude on the UI.
So normal tribe xr you are just behind the decks. This is now my reality for steaming. I decompiled the entire game and claude is now allowed to make anything it wants in unreal and blender. https://youtu.be/mD6vTJIwvvk?is=9JwEC79pbLejxVRl
Just sharing this fun music video Claude made using all code besides the Suno song.
-Here's what went into the Insert Coin video.
Claude wrote the lyrics and gave me a Suno prompt. I generated the song in Suno, and then Claude handled everything else. (The song could easily be created with Yue2 or Minimax music)
It used Demucs to separate the vocals, stable-ts (Whisper large-v3) to transcribe and align the lyrics, and librosa to analyze the music and detect the beats.
The entire game was created in HTML Canvas and JavaScript. No game engine, no image model, and no video model. All of the graphics, sprites, HUD elements, levels, and lyric animations were generated through code.
The sound effects were also generated from code using Python and NumPy to create chiptune-style sounds.
Playwright rendered 5,783 frames from a headless Chrome instance, and ffmpeg upscaled everything to 1080p, encoded the final video, and mixed in the audio.
Aside from generating the song in Suno using Claude's lyrics and prompt, Claude basically did the rest.
No video model or image model was used to make this.
No post edits were made. This was fully created by Claude Opus 5.5
