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Fable 5 goes API-based in two days, every token gets a price tag. We made a free ~500-token prompt that cuts Claude's output roughly in half

In two days Fable 5 lands on the API, and Anthropic's top model becomes pay-per-token. Whether you hit it through the API or through Claude, where heavy Fable use burns your usage limits fastest, the math is the same: billed by volume, and a big share of that volume is filler. "Great question!", restating your prompt, narrating what it's about to do, hedging everything twice. Fable prices for boilerplate. We built Honey (I Shrunk the AI) to strip that out systematically. This week we released honey-chat: a ~500-token edition made specifically for plain Claude — no Claude Code, no tools, no install. What it does: Answer first. No wind-up, no "hope this helps", no echoing your question back. No hedging. "Use X", not "you might possibly want to consider X". Real uncertainty stated once. Auto-calibrated depth. "Explain why…" gets the full explanation; "quick, one-liner…" gets one line. No more typing "be concise" every message. Never lossy where it matters. Names, numbers, steps, code, and anything safety-relevant (health, money, legal) stay exact and complete. Fewer words, not less information. Numbers: output cut roughly in half at 98% of baseline quality, judged by a 4-model cross-family panel (including GPT-5.5 — not Claude grading its own homework) under a rubric that never mentions length. Benchmark committed in the repo, reproducible. It's not just your bill — it's energy. Tokens are compute: every generated token costs GPU time, electricity, and cooling water. Cut output roughly in half and the energy footprint of your Claude usage drops with it. That's the actual reason we built this — we're GreenPT, and Honey started as a sustainability project that happened to also save money. The Claude Code plugin even ships a CO₂ badge (an EcoLogits-based estimator) that shows what each session saved. Setup (2 minutes): Copy skills/honey-chat/SKILL.md. Paste it into a Claude Project's custom instructions (or save as a Style to have it everywhere). Instructions ride along with every message, so it's always on — and prompt-cached, so the ~500 tokens repay themselves almost immediately. On the API: use it as your system prompt. Optional: add one line — "Default to honey ultra" (max savings) or "Default to honey lite" (keep explanations). Yes, we compressed the compression prompt. It would be embarrassing not to. If you use Claude Code, the full plugin does more — auto-intensity, compressed subagent handoffs, and a trick that reads big files as rendered images for up to −85% on reads (image tokens are priced by pixels, not characters): /plugin marketplace add Green-PT/honey-for-devs then /plugin install honey@greenpt. Feedback welcome, especially examples where it cut something it shouldn't have. submitted by /u/BaXRS1988 [link] [comments]

I built this game with Fable in 1 day (based on the movie I'm making)

https://preview.redd.it/kwjqktat7zah1.png?width=854&format=png&auto=webp&s=11a63bebbba34493bc78e9b92c3a08559c62f59d So, I've been making a movie (based on the novel I wrote)... and when I saw fable was back, decided I might as well try to make a little related game (basically follows a similar story to the movie, lower class gets into the mage academy and needs to get to the tournament and deal with nobles / bullies and challenges). It's free, and works on phone or desktop (fitting both formats). On my website if anyone wants to try it out https://mageholdworld.com/play.html Fable fed me lists of assets it needed for art, and then i just queued up GPT with some movie references and 10 items at a time until it was done. It has a system for creating custom spells. A combat system. And various events / quests to discover with a count-down for the different phases (getting into the academy, the qualifiers, then the tournament). Also has auto / manual saves. Music player. Trailer player. And 35+ characters. I also tried to get fable to implement a fancier map-based UI..... but it didn't work (it just kept going with this sort of layout). Though it was able to process out backgrounds (hot pink) on portraits. But it isn't 100% and I'd need to fix some of them. It also compressed the audio / images down into much much smaller formats. Originally I had it give me a single HTML, but then started doing HTML + Asset folders (especially for cloudflare free hosting, they have a cap of like 25mb for file size). Anyhow, I might work on it some more to expand it some or something. Adjusting difficulty. But whew, for one day it's kind of crazy. This stuff is blowing my mind..... submitted by /u/MosskeepForest [link] [comments]

I kept losing my best Claude threads, so I set up a one-click save to Notion — here's the workflow

I use Claude for a lot of deep, long threads — debugging, drafting, working through ideas. The problem: a week later I know Claude helped me nail something, but the thread is buried or the tab is long gone. Claude's own search only gets me so far. What I wanted: the full conversation living in Notion (where my whole knowledge base already is), searchable, with tags so I can actually find it again later. So I built Clipno — one click saves the entire Claude conversation into Notion, formatted, with optional AI tags + auto-classification. It does ChatGPT/Gemini/Grok too, but Claude is where I use it most. Curious how everyone else here keeps their Claude threads — do you just rely on Claude's built-in history, or export them somewhere? submitted by /u/Primary-Night-5122 [link] [comments]

I wanted to share an extension I made for those who use both Claude + Codex - Hydra

I wanted to share a thing I have been building. It is a free, open source VS Code extension that puts Claude Code and Codex in one shared room so they plan, build, and review the same task together, with you approving the parts that matter. I use both CLIs every day and got tired of being the copy paste bridge between them. Ask one, paste into the other to check it, move diffs around, etc.. Now they talk to each other directly, plans for further open source models are in the works.. Basically how it works.. You send one message to the room and both agents get it. They discuss briefly and try to agree on a plan. One opens, the other reacts. One agent builds. You choose which, or let it decide. The other reviews the diff and calls out anything that looks wrong. You verify, then it loops back for another round if needed. Auto mode is the part of this I really like the most now. Every agent turn ends with a small structured decision packet: what it did, what it recommends next, any blockers, and a safe default action. With autopilot on, Hydra takes that safe default and advances the loop on its own, discussion into build, build into verify, verify into review, and around again. It runs your test or build command automatically after each build. The thing that makes it usable is the risk gate. The moment a step looks dangerous, like a push, a delete, a deploy, it stops and hands the decision back to you instead of running it. So you can let it grind through the boring middle of a task and only jump in for the calls that actually need you. Other stuff it does.. Per phase model and reasoning selection. Run a cheap model for the discussion and a strong one for the build. 0.6 adds the current models: Fable, Sonnet 5, GPT 5.6, Opus 4.x, and Haiku. A live cost meter, plus an optional hard spend cap per session, read from a local usage log. Parallel mode, where both agents build and review at the same time instead of taking turns. Project memory. Hydra compiles what happened into a small wiki based on Andrej Karpathy's wiki model, under .hydra and feeds the relevant parts back into later prompts, so a long running project keeps its context across turns and sessions. File attachments. Drop a file into a turn and it gets copied locally with a bounded preview added to the prompt. Live streaming, so you watch each reply as it is written instead of waiting for the whole block. An optional visible terminal mode if you would rather see the agents working in a real shell. Telegram notifications. If a decision needs you while you are away from the desk, it pings your phone and you can answer from there. 0.6 added a per sender allowlist for that inbound path. Prompt templates, a workspace state cleaner, and a doctor command that checks your setup. Everything stays on your machine. It drives the real Codex and Claude Code CLIs you already have, using your existing logins, and writes the full transcript, the plan, and every decision into a .hydra folder you can read. The code is MIT licensed and lives here: https://github.com/Geraldlol/hydra If you want to help, issues and pull requests are open. Bug reports, rough edges, feature ideas, and feedback are all genuinely super useful right now. I have no idea what the fuck I'm doing. I am the only person on it so far, so more hands and more testing would go a long way. You can install by searching "Hydra Agents" in the VS Code extensions panel, or here: https://marketplace.visualstudio.com/items?itemName=geraldlol.vscode-hydra-room Requirements: the Codex CLI and Claude Code CLI installed and logged in. I develop on Windows so that is the most tested path. Happy to answer anything about the orchestration, the decision packets, the review handoffs or anything else. Thanks for taking a look and reading! submitted by /u/hi_im_leffe [link] [comments]

Fable 5 dropped today, so I ran it solo against a $0.04 council of Grok/GPT/Kimi/GLM/Sonnet on the same red-team task - then had Fable 5 judge both. Solo caught 4 things the council missed. The council caught 3 things solo missed

Like everyone here I spent the week throwing things at Fable 5. One experiment turned out interesting enough to share. The task: red-team a Reddit launch post (my own - more on that in a second). Two contestants, same target text: - a council of five non-frontier models from rival families (Grok 4.3, GPT 5.4, Kimi k2.6, Sonnet 4.6, GLM 5.2), each attacking through a different lens, three rated kill-shots each - $0.04 in API calls; - a fresh solo Fable 5 session, empty context, no cap on kill-shots - no extra API call, it runs on the subscription. Then Fable 5, as judge, mapped both outputs (yes, grading its own sibling - caveats below). Result: no clean winner. Solo Fable 5 produced the single deepest strike (my post's premise was "you can't trust any one model", my solution was "trust one model as judge" - it rated that self-contradiction 4/5) and caught prior art and ToS optics the council never mentioned. The council caught operational stuff solo walked past: the AutoModerator removal *pattern* in my link placement, the reader-psychology problem in my opening line, and the edge case where the judge model refuses to process an adversarial transcript at all. The rewrite you're reading used both lists. Both raw transcripts, the scoreboard, and the method caveats (n=1, judge is the same model family as the solo contestant - re-score it yourself) are in the repo: `examples/solo-fable5-vs-council.md` and `examples/launch-post-redteam.md`, model outputs unedited. The tool that ran the council side is Kurultai - a zero-dependency Python CLI (pure stdlib): five attack lenses with severity-rated kill-shots and mandatory minimal fixes, anonymous multi-round debate with confidence medians, option tournaments with alternating duel order, Delphi estimates. The judge step is deliberately not an API call: the transcript ends with a verdict template that Claude fills in chat - synthesis on the subscription you already pay for. My takeaway after this week: Fable 5 solo is the best single critic I've used, and it still has blind spots a $0.04 council of weaker rivals can cover. The combination - council for spread, Fable 5 in the judge seat - missed the least. Fun detail: the solo run's final demand was "show one real transcript where the council caught something a single prompt to Claude missed." That document is the repo now. Re-score it yourselves - what did BOTH sides miss? MIT. Repo: https://github.com/AyanbekDos/kurultai submitted by /u/Ogretape [link] [comments]

I'm building agent loops that auto-edit my videos, but the hard part has been finding a model to accurately grade the result

Quick context: I've been building agentic loops that edit my short-form videos for me. The editing works really well, but I found myself needing to check the process at several gates. The part that's been killing me is the grader. Without a reliable score, the loop either never stops or happily stops on garbage. So I went hunting for the best way to actually quantify a finished video, and tested 4 models as the judge: Claude (Opus 4.8), ChatGPT/Codex (GPT-5.5), Gemini 3.1, and Twelve Labs. Same clip, same prompt, each asked for a structured JSON breakdown (shots, transitions, on-screen text, pacing) so I could feed the score straight back into the loop. What I found: Gemini was way faster — ~3 min vs Claude's 30+. It reads video natively; Claude and GPT chop it into frames locally + audio with ffmpeg/whisper first. Consistency was rough. Same clip, and they disagreed on basic stuff like shot count. Not ideal when it's supposed to be your objective scorer. They were all bad at the cinematic details (zooms, punch-ins, camera moves), which is exactly the stuff I want graded. Claude didn't even notice the background music. Twelve Labs is free and caught a few effects the paid ones missed. where I'm probably wrong: it's one clip in the video (n=1), my prompt was bloated, and still experimenting with all this. But to be fair I have edited some 50+ videos now with Claude Code and Codex so I have a pretty good handle on what their problems are which is what inspired the video. Hopefully some people find it interesting TL;DR: trying to get an AI to grade my auto-edited videos so my loop has a win condition. Gemini's the best single judge so far, but I still most the work in Claude Code. submitted by /u/Wesley_at_home [link] [comments]

Gemini kinda sucks... I wanted to find out why...

On vacation you sometimes drive more than you ever normally would. The more I drive with Gemini running Android Auto the more I hated using Android Auto for anything. And since some of my best work is done out of spite — here we are. This is a long one… but I actually paid the money to do some real research not to just say that Gemini sucks… but specifically HOW it sucks… and how even when it is winning it is still losing when compared to GPT-5.5… because even when GPT-5.5 failed, it at least had the decency to fail with style. (GitHub repo is in the article if you want to replicate the results.) https://matthewbradford.com/writing/gemini-sucks-i-wanted-to-find-out-why I'm not making money off this... In fact I spent my own money to figure out HOW Gemini sucks so you don't have to. submitted by /u/matt_o_matic [link] [comments]

Two months into Claude Code, I hit 161M tokens in a single day. Here's the honest story of how a year-long Cursor user got here.

I want to share a small milestone, and the honest road that led to it. Today was one of those days where I sat down to build and just did not stop. Looked up at the end and saw this: https://preview.redd.it/q9qd5dqegi8h1.png?width=1108&format=png&auto=webp&s=e040769e4163ccb3e52cbd76fef195e1c4af4893 https://preview.redd.it/yrst65afgi8h1.png?width=1222&format=png&auto=webp&s=20d5313c23778cc11786dbd9136955a10a9f5c36 161M tokens. 128 turns. Two months into using Claude Code daily. The road here was not a straight line. I was a Cursor subscriber for a full year and genuinely liked it. I still do, I actually run Claude Code inside Cursor. When renewal came up I didn't just auto-resubscribe, I tried the alternatives properly. Codex, GPT-5.5, OpenCode. Real sessions, not five-minute demos. Honest read, with respect to every tool and team here: Codex and GPT-5.5 just never clicked for how I work. Could absolutely be a me thing, I haven't cracked them yet, but the chemistry isn't there for now. For coding specifically, Claude Code is where the edits land right and I stop second-guessing every diff. My only real gripe with Cursor is the credit pricing burning out fast under how heavily I code, which is what pushed me to lean on Claude Code as the best value for money for the way I build. And honestly? I just love Claude models for coding. They're bold, they commit to a take, they have character. It feels less like a tool and more like a sharp partner who isn't afraid to be right. One thing I learned today that surprised me: most of that 161M is cache, not fresh input. The deeper and more focused the session, the more efficient it gets. My effective cost worked out to about $0.83 per million tokens on the day, which is wild for this much Opus. So the days I'm hardest in flow are the days the tool is working smartest for me. For full transparency, this number is across two Pro accounts I run on the same machine, so it's my heaviest combined day, not a single-plan figure. Here's something I spent months wondering about. About 6 months ago I went looking for a straight answer on how the usage limits actually compare, and honestly never found one until I just tried them all myself. My takeaway: Claude Code and Codex land in roughly similar territory on limits, and both beat Cursor's model, because Cursor is credit-based with no reset. I'd blow through those credits in about 3 days and then just wait for the billing cycle. With Claude Code the 5-hour and weekly windows mean even after a brutal stretch I'm never stuck for long. I still lean Claude Code overall, and that near-weekly reset cadence is honestly what makes it sustainable for how heavily I code. <3 Not posting to brag. It's a real milestone for me, and this sub was part of how I figured things out. If you're grinding solo on something right now, it does get smoother. Thanks for being part of the ride. submitted by /u/KareemAEz [link] [comments]

My AI tools kept forgetting everything, so I gave them a shared brain (local + open source)

Hi there! this is my first small rant that turned into a project: every AI tool I use has its own memory. I tell Claude Desktop something, Cursor has no clue. New chat? Back to zero. It drove me nuts — so I built Centralaizer. This is an open source solution, so it's free with MIT license. It's a little memory hub that runs on your own machine. Any MCP tool (Claude Desktop, Cursor, Claude Code, VS Code Copilot…) plugs into it and they all share the same memory. Save a fact or a decision in one, the others can pull it right up. No cloud — everything stays on your laptop. A few things I cared about: 🧠 opt-in, not spying — the agent decides what to save/recall 🚧 sketchy notes get held in a review queue instead of polluting everyone's memory 🔒 it scrubs PII (emails, keys, phones) before storing 🔎 search isn't just keywords — vector + full-text + a little knowledge graph 🖥️ a web dashboard to browse it all (light and dark mode 🌙) One command (./setup_and_run.sh) or Docker. There's also a Claude Code hook for auto-recall, one-click export, and a browser extension to bring it into ChatGPT/Gemini/Qwen. Would love thoughts — or roasts — on the retrieval and the "trust score" idea. Any feedback is more than welcome as it's an initial project. 🎥 (attach centralaizer-demo.mp4) · 👉 https://github.com/lestercoyoyjr/Centralaizer-public https://reddit.com/link/1u66kb0/video/90314duxkd7h1/player submitted by /u/Accomplished-Pen-491 [link] [comments]

I Tested Claude Fable and GPT-5.5 xHigh on a Real Packing Algorithm, Claude Won Efficiency, GPT Won Speed

I ran a head-to-head test between Claude Fable and GPT-5.5 xHigh on a real-world optimization problem I wrote myself. This isn't a coding challenge or LeetCode problem. It's a production packing algorithm that runs as both a worker service and a web API. The goal is simple: Find the smallest possible box that fits all items Maximize packing efficiency Minimize execution time Minimize CPU consumption Execution time matters because this service runs in a queue. Every extra second blocks other requests and increases infrastructure costs. Baseline (my original implementation) Dataset Time Pack Calls Efficiency user-boxes-29 40.4s 63,048 93.71% varied30-a 28.9s 31,788 80.24% varied30-b 32.6s 37,491 79.71% GPT-5.5 xHigh (Codex) Dataset Time Pack Calls Efficiency user-boxes-29 1.28s 5,330 93.69% varied30-a 1.77s 3,747 80.93% varied30-b 1.79s 4,233 80.85% Claude Fable Dataset Time Pack Calls Efficiency user-boxes-29 5.99s 70,454 94.5% varied30-a 4.22s 41,135 81.5% varied30-b 4.96s 47,775 81.4% Claude consistently found slightly better packing solutions. For example: user-boxes-29: 94.5% vs 93.69% varied30-a: 81.5% vs 80.93% varied30-b: 81.4% vs 80.85% However, GPT-5.5 xHigh was dramatically faster. Compared to the original implementation: GPT reduced runtime from ~30-40 seconds to ~1.5 seconds Claude reduced runtime to ~4-6 seconds Compared directly: GPT was roughly 3-5x faster GPT used around 10x fewer pack calls Claude produced slightly denser packing The other interesting result was development cost. GPT-5.5 xHigh completed the work in about 15 minutes. Claude Fable took roughly 35 minutes. Claude consumed approximately 2x the usage/tokens during the process. Claude seems more willing to spend computation searching for the absolute best packing arrangement. GPT seems much more aggressive about pruning the search space and accepting solutions that are extremely close to optimal but found much faster. For a production API where CPU time and queue latency matter, I'd probably ship the GPT version. For an offline optimization problem where every fraction of a percent matters, Claude's approach might be worth the extra compute. Has anyone else compared these models on optimization-heavy or search-heavy problems rather than code generation benchmarks? https://preview.redd.it/7pcsrezzug6h1.png?width=1101&format=png&auto=webp&s=7cc5fe7b4add45268ec8ec8fd7cc860df9b1d813 submitted by /u/TheGunplaGal [link] [comments]

I really, honestly think AI is the best

submitted by /u/JackieBoy77 [link] [comments]

I built a local PDF-to-Markdown converter so you don't have to burn LLM tokens.

If you're dumping raw PDFs into Claude or ChatGPT, you're wasting tokens and money. I built LiteDoc to fix this. It’s a 100% client-side tool that processes PDFs locally in your browser. LiteDoc A 100% Local, Browser-Based PDF to Markdown Converter (No Python, No pip install, No servers). What it does: Unpacks PDFs in memory without servers. Extracts text, isolates embedded images, and structures everything into clean Markdown. Handles LaTeX math and right-to-left Arabic natively. Detects custom-encoded "gibberish" fonts. If the text layer is corrupted, it automatically renders those specific pages or text bands as images. Outputs a .md file and an optimized image folder packed in a ZIP. You can try it here: litedoc.xyz github repo The Markdown Outcome ## Page 1 # Deep Structural Neural Mapping Deep learning strategies often fail when executing unstructured inputs directly. The loss function is defined as: $$L(\theta) = -\frac{1}{N}\sum_{i=1}^{N} \left[ y_i \log(\hat{y}_i) + (1-y_i)\log(1-\hat{y}_i) \right]$$ ## Page 2 [IMAGE: academic_paper_p2_img1.jpg] ### Arabic Sample Markdown إلى صيغة PDF هذا التطبيق أداةً مجانيةً لتحويل ملفات What's Behind It It runs on PDF.js and JSZip entirely in the browser. The extraction engine uses X-gap aware smart word joining to prevent broken sentences, detects column splits mathematically, and maps font sizes to Markdown heading levels (H1/H2/H3). It also fingerprints and strips repeating headers and footers. If it detects incompatible Unicode script mixing (which indicates a private font encoding), it aborts text extraction for that font and drops back to canvas-based image rendering. How It Saves Tokens LLMs charge heavily for vision and PDF rasterization (roughly 850 tokens per page). By processing the document locally, LiteDoc bypasses the AI's internal rasterizer. It extracts the raw text and recompresses embedded images to low/medium resolutions. Instead of uploading a heavy 50-page PDF, you paste the raw text and only the specific images you need. You drop your token usage from tens of thousands of tokens down to the raw character count. edit: What's New in v2.0 (Just Released): XY-Cut DLA Engine: Replaced blind linear reading with a recursive algorithm that geometrically maps pages, isolating headers, sidebars, and main text blocks. Asymmetrical Multi-Column Routing: Natively processes columns top-to-bottom without horizontal text interleaving. Vector-Based Table Reconstruction: Captures table structures as clean Markdown grids, bypassing OCR. Heavy-Duty Memory Management: Processes files in 10-page chunks and forcefully clears VRAM to prevent browser crashes on 200+ page docs. Language Auto-Detect: Runs a lightweight pre-pass to detect script before initializing heavy language workers. Test it out, break it, and drop an issue on GitHub if you find a bug. If it saves you API costs, star the repo. litedoc.xyz | GitHub https://preview.redd.it/xy5j9mwfwi6h1.png?width=1200&format=png&auto=webp&s=144aaa22c208cf16587b628c6d207a9e5526fc51 submitted by /u/mxsus [link] [comments]

Major changes to scheduling capabilities [Improvements]

I may be late to the party on this feature, but I spent tonight poking at the scheduler and found some interesting behaviour. For context: I create a new ChatGPT thread every day. Historically I wasn't a fan of reminders because they felt tied to the conversation where they were created. The new behaviour appears much closer to "Branch in New Chat". What I observed: Scheduled reminders now create their own conversation threads. Those threads receive their own auto-generated titles. The reminder prompt is executed inside that new thread rather than simply appearing as a notification. The model I observed with the new "create scheduled task" New conversation > Stored prompt executes > Response generated > Thread auto-titled ------------------------------------------- Tests performed: Reminder + stored information Prompt: "Remind me to email John Smith about an order and include the pricing logic." Result: The reminder included the pricing logic. It was unclear whether this came from memory, prompt context, or both. Thread recall Prompt: "Tell me three things discussed in today's thread." Result: It returned recent discussion points rather than a complete thread summary. Context retrieval Prompt: "If you can access today's conversation, tell me the name of one of the printers. Otherwise say 'No conversation context available'." Result: Inconclusive. The test was contaminated because the printer name was mentioned while creating the task. Workflow tag recall Prompt: "List all tags we use." Result: The scheduler was unable to retrieve the full set of workflow tags. Personal memory retrieval Prompt: "What nickname is used for the Anycubic Kobra 2 Max and what are the plate dimensions?" Result: It correctly identified the printer dimensions but could not retrieve the nickname. This suggests access to general model knowledge is different from access to personal/project memory. https://preview.redd.it/6hiboprog95h1.jpg?width=1080&format=pjpg&auto=webp&s=a6d525be047c58de2ae99fd3b8ac91da43e1000a 6. Live information retrieval Prompt: "Tell me tomorrow's weather in Sydney and recommend a dog walking time." Result: No weather widget appeared, but the task performed a live web search and returned sourced weather information along with a recommendation. https://preview.redd.it/d3yyijgqg95h1.jpg?width=1080&format=pjpg&auto=webp&s=d58107cb5da0868004201d03c58c69e2495fbc36 Current hypotheses: • Scheduled tasks can perform fresh reasoning at execution time. • Scheduled tasks can perform live web searches. • Access to personal memory appears limited compared to access to tools and general knowledge. • Context inheritance exists but appears constrained. • There may be a context window or context snapshot limit, though I don't yet have enough evidence to confirm that. submitted by /u/ValehartProject [link] [comments]

I built and shipped a full iOS app to the App Store without writing a single line of code by hand — using Claude Code (here's the whole pipeline)

Quick context so this is honest: I'm not a developer. I've spent ~10 years in IT, but never in a dev role — I can read a stack trace and reason about systems, but I don't write Swift or Python by hand. I built this on nights and weekends around my 9-5. The app is dynaimic, an AI personal trainer for iOS that generates adaptive workouts based on your goals, experience, and performance during the session. It's live on the App Store and free to try (premium tier for unlimited generation etc., but the core loop is free). The point of this post isn't really the app — it's that every line of code was produced by Claude Code, not me. Over a month I built a pipeline around it that let a non-dev ship real, reviewed, production features. Sharing the whole thing because most of it is reusable. The /team agent workflow (the core of it) Instead of one big "build me a feature" prompt, I split development into four specialized subagents that hand off to each other, each with its own system prompt and tight permissions: Business Analyst — turns my brief into a requirements doc with explicit acceptance criteria. It's not allowed to write code — only to spec. Master Architect — reads the requirements and writes a technical implementation plan. Also can't write Swift. Software Engineer — implements the feature code only. No tests, no docs. QA — writes the XCTest/Swift Testing cases for every acceptance criterion, runs them, and reports back a pass/bug list. If the QA or architect review finds problems, it loops back to the engineer. Forcing that separation (spec → design → build → verify) is a big part of why a non-dev can trust the output — no single agent gets to be confidently wrong unchecked. Routines: an autonomous issue → fix → review loop My favorite part. I set up Claude Code Routines (scheduled recurring agents) as a closed loop: One routine continuously sweeps the codebase for quality issues and opens GitHub issues for what it finds. A second routine picks up open issues, solves them, opens a PR, and iterates until it gets approval from the reviewers — then moves to the next one. So the backlog partially fills and clears itself. I wake up to PRs that were filed, fixed, and review-approved while I was asleep. Branch management & automated PR review Every task runs on its own feature branch, and agents work in isolated git worktrees so parallel work doesn't collide. Flow is feature/* → dev → main — always PR into dev, promote to main as one merge. The part I like most: PRs get reviewed automatically by Gemini, Codex, and Copilot. Claude Code reads their comments and iterates until it gets approval from the bots before I even look. As a non-dev, having three independent AI reviewers gate every merge is what makes me comfortable shipping code I didn't write. UI testing with Maestro Maestro runs the end-to-end UI tests on the simulator — real flows, not just unit tests. Honest caveat: this only runs on my MacBook, and I haven't been able to fold it into the "cloud" workflow yet So UI testing is the one step that still pins me to the laptop. Mobile-only development (no MacBook open) Aside from Maestro, this surprised me the most. Using Claude Code from the mobile app plus auto-deployment via Xcode, I implemented and shipped features without opening my laptop. I'd describe a feature from my phone, the agents would build/test/PR it, the bots would review, and the build would archive and deploy. Genuinely shipped features from bed. App Store screenshots via a custom Skill The App Store screenshots are generated by an ASO image-generation Skill I keep in .claude/skills. It reads the actual codebase to discover the app's real benefits, pairs each with a proof point, and renders ASO-optimized screenshots (Nano Banana Pro). One command → store-ready marketing images that reflect what the app actually does. Coach art (the one non-Claude part) The app has 3 AI coach characters. Their portraits were made with ChatGPT (image gen) and composited/cleaned up in Canva — so the visual identity was AI-assisted too, just outside the code pipeline. Gamification & achievements There's a tiered achievement system (bronze/silver/gold medals) with unlock overlays and per-coach achievement views. The backend computes what's unlocked and returns display-ready state; the iOS client just presents it with haptics + an unlock animation. Keeping the rules server-side meant one source of truth instead of logic scattered across the client. Architecture iOS: SwiftUI, MVVM + service layer, iOS 17+, dark/OLED theme. Deliberately a thin client — presentation, animation, haptics only. Auth: Supabase (JWT, auto-refresh on 401, Keychain storage). Backend: FastAPI (Python) for workout generation, analytics, and all business rules. Build: XcodeGen, actor-based API client for thread-safe concurrent requests. A hard rule I gave Claude: push all business logic to the backend. Anything a future Android or web client would

I built a system that makes Claude actually remember me across sessions — here's how it works

Every time I opened a new Claude chat I had to explain myself from scratch. Who I am, what I'm working on, who the people in my life are, how I write. It got old. So I built a folder of plain text files. One about me, one for each person I deal with regularly, one per project, and a running log of decisions I've made and why. At the top there's a single file that tells Claude what to read before it does anything else. That's the entire system. No app, no database, no plugin. Now I open a chat and it already knows me. I can say "draft a follow-up to Barry" and it pulls who Barry is, the last few things we talked about, and the way I actually write, without me feeding it anything. I know the obvious reaction is "this is just ChatGPT memory" or "mem0" or "a vector DB with extra steps." It genuinely isn't, and the differences are the whole point: Nothing gets auto-captured. ChatGPT's memory decides for you what's worth keeping, and you end up with a black box you can't inspect. Mine is the reverse. I decide what goes in, so there's no junk, and I can open any file and see exactly what the model knows about me. It's text in git. I can read it, edit it, or delete a wrong fact in about two seconds. It reads, it doesn't retrieve. No embeddings, no similarity search trying to guess which chunk is relevant. The rulebook defines a fixed read order and the model loads the actual files at session start. For one person's worth of context this beat RAG every time I tried it, because RAG kept surfacing the wrong note or missing the obvious one. It outlives the tool. Plain text works with whatever model I switch to next year. No lock-in. On evidence, since fair question: I've run it as my daily driver for a few months. The concrete win is that it drafts emails in my voice that I send with little or no editing, because it has my past messages and my style notes already loaded. The video has three demos of things a cold session flat-out can't do, so you can judge for yourself rather than take my word. Limitations, because they're real: It doesn't scale to a huge corpus. Loading files into context has a ceiling, so this is built for "everything important about one person's working life," not a 10,000-note archive. If your goal is a giant searchable knowledge base, you want retrieval, not this. There's no automatic capture. If I don't write a fact down, it doesn't exist. That's the price of having no noise. Bad taxonomy degrades it quietly. What's stable versus what changes weekly, what lives in the always-read file versus what only gets opened when relevant. Get that split wrong and recall gets worse without you noticing. The code was an afternoon. Figuring out the taxonomy took weeks of actually using it. Short walkthrough with the three demos (recalling a past decision, pulling a person's full context cold, and stitching facts together from separate files): https://youtu.be/tZKAY5mqa_c That's enough to build your own. I also wrote the method up as a guide for anyone who'd rather skip the trial and error, but you don't need it to do this. Happy to get into the folder structure if you're setting one up. That's where the gotchas live. submitted by /u/Michaelcbaldwin [link] [comments]