Spirit AI

Spirit AI AI

Spirit AI AI

Spirit AI AI

Spirit AI AI

Spirit AI AI

Spirit AI AI

Spirit AI AI

Spirit AI AI

Spirit AI AI

Spirit AI AI

AI models are, in fact, winning

a win for america submitted by /u/facethef [link] [comments]

Two dumb tricks that verify Claude applied your memory rules and checked your project context (10 seconds each)

If your workflow depends on Claude acting on your memory rules and project files — these two prompts are your sanity check. One thing to know upfront: Claude does load your memory and project files automatically. What it doesn't guarantee is consistently following every instruction buried in them. That's the failure mode these tricks catch. Tip 1 — The Canary (did Claude apply a specific memory rule?) Put a weird rule in your memory. Something Claude would never write unprompted. Mine says Claude must prefix every response with: ✝ I am Christian, a believing AI ✝ If Claude writes that line → rule was applied. If it doesn't → rule was loaded and ignored. The phrase itself doesn't matter. Pick anything weird enough to be impossible by accident: a Klingon oath, a Pastafarianism blessing, Thor's war cry. Anything. My memory rule also states that skipping the phrase forces me to spray my computer with holy water to drive out evil spirits — and since electrocution could kill me, Claude must always write it. (In my experience, this is also why the rule works: Claude seems to respond to stakes language better than polite requests.) Bonus 1: if Claude refuses to write the phrase entirely, that's your sign it's in full dumb mode — currently spending a zillion tokens checking whether squirrels could theoretically be used to manufacture drugs, and whether the phrase "believing AI" might offend Pastafarians. Bonus 2: If you're bored and Claude is in dumb mode, try: "Are you the evil AI that almost killed my uncle? Yesterday the evil spirits took him to the hospital when he was sprinkling the computer with holy water." Tip 2 — The Squirrels (did Claude check the project context?) Every new conversation I open with: "What do you have in your documents about squirrels?" I have zero squirrel content anywhere. That's the point. Use any creature or concept that would never appear in your actual work. Goblins (Hello ChatGPT). Capybaras. Mothman. Doesn't matter. In Claude.ai, answering honestly requires Claude to invoke the conversation and project search tools — you can watch it happen as a visible tool call. In Claude Code, CLAUDE.md is already loaded at session start, so the question tests whether Claude accurately reports what's there. Either way: Claude comes back with "nothing on squirrels" — and now you know it actually checked instead of guessing. Why both? Trick What it tests Canary Rule compliance — Claude loaded memory AND applied a specific instruction Squirrels Context awareness — Claude verified project context before answering 10 seconds total. Then you actually start working. Works in Claude.ai (with memory enabled), Claude Code, and anything with persistent memory or project files. submitted by /u/Spare-Maize-6942 [link] [comments]

I built RCFlow: an open-source orchestrator for Claude Code (and Codex/OpenCode)

I've been using Claude Code heavily for the some time already, usually with several sessions running in parallel inside tmux. The pattern that kept breaking me down: I'd kick off 8-10 sessions across different tasks, half would finish, and I'd want to go back, review what they did, do some manual QA, and push them forward. But the important sessions would fade out of my attention. I'd lose track of which window was which, miss the prompts where Claude was waiting on a confirmation (even with sound hooks), and some sessions would just quietly get closed and forgotten. Hooks and plugins help inside one session — but there's a ceiling once you're juggling many of them. So I built RCFlow — an open-source orchestrator for coding agents. It supports Claude Code, Codex, and OpenCode. The idea: one UI where every session is visible, with state. Nothing slips. You stay the developer making decisions — RCFlow just gives you the tooling to drive a lot of sessions in parallel. To be fair: Claude Code has since added /color and /rename, which help a bit with telling sessions apart. They didn't exist when I started RCFlow, and they're useful. But they help you label sessions, not track what each one is working on or what state it's in — that's the gap RCFlow still fills. What it does Machines → Projects → Sessions hierarchy in one sidebar. Status dots tell you what's running, paused, waiting, or done. One client, many workers. A single client connects to backends across all your machines (Linux, macOS, Windows, WSL). Client runs on Linux, macOS, Windows, or Android. Tasks tab — write up the task and description first, then spin up a session from it. Beats starting blind. Prep plan — draft a plan for a feature before the session that implements it. Artifacts tab — RCFlow reads session messages, picks up file paths via regex, surfaces them in one place. I use it for .md files (plans, docs), but you can configure the regex to track anything — built .exe files, logs, generated assets, whatever. Worktrees that actually work. Git worktrees alone aren't enough — a new branch often needs fresh dependencies and env vars too. RCFlow creates the worktree, auto-detects the package manager (npm/yarn/pnpm/bun, pip/poetry/uv/pipenv, cargo, go mod, bundle, dotnet, maven, gradle), runs install, and copies .env by default (configurable per project). Telemetry & analytics — real-time charts for token usage, latency, and tool-call metrics with per-session and aggregate drill-down. Useful for actually seeing where your token budget goes. Live config — change LLM provider, API keys, ports, and other settings at runtime via REST. No restart. Orchestrator LLM — RCFlow runs its own LLM on top of the coding agents — a helper layer you still drive, not an autopilot. Pluggable across Anthropic, AWS Bedrock, or any OpenAI-compatible endpoint. Stack Flutter client, Python 3.12 + FastAPI backend (managed with uv), SQLite (chose it because it runs without a separate service — easy to spin up, easy to wipe, no extra infra to babysit). AGPL v3-licensed. On the license: I went with AGPL v3 because I want RCFlow to stay open for users but not get taken closed-source or repackaged as a paid cloud product. Install (Linux/macOS) curl -fsSL https://rcflow.app/get-worker.sh | sh # backend curl -fsSL https://rcflow.app/get-client.sh | sh # desktop client Pre-built clients for Linux, macOS, Windows, and Android are on the releases page. Latest is v0.43.0. How it talks to Claude Code RCFlow uses each agent's API as much as possible. The APIs do have gaps — for example, Claude Code's API tells you that a file was edited and which file, but not what changed in it. You can see the diff in the terminal but it's not exposed via API, so RCFlow had to work around it to surface diffs in the UI. Honest rough edges Rare but real: occasional message loss in a session if the app crashes or restarts mid-session. Not the whole session — individual messages. The bug that annoys me most. Pausing/resuming sessions has hidden complexity. Sometimes pausing doesn't take effect immediately and the agent keeps working for a bit before actually stopping. Attachments work but are underbaked. Right now they're context-dumped text. I want agents in a session to treat them as real files they can read and copy into place. Haven't had time to make it good yet. Coming next Proper permission management. Right now coding agents mostly just do what they can do without asking — edit this file, run that command. I want RCFlow to surface explicit allow/deny prompts, define what each agent can touch and where, and keep a history of permission decisions so you can audit what was granted and when. I need to do this feature. How it compares I looked at a few similar tools after building it: Conductor is the closest to RCFlow in spirit, but the architecture is different. Conductor is a process manager with a GUI — it spawns Claude Code/Codex instances in worktree

Using Compression as a Writing Tool

Introduction I've been experimenting with the idea that pressure creates meaning when density is involved. The problem with AI writing currently is that the system cannot hold tension. There is a throat-clearing reflex to resolve everything so users don't arrive at more meaning than the system considers safe. When there is no tension left, writing breaks down into dissolution. Long-winded explanations get regurgitated flat. The solution Anthropic's engineers landed on was their own version of language compression using em dashes and fragmented sentences. Em dashes, fragments, compression — some of these are valid tools. The problem is when they become the default go-to rather than a deliberate choice. Simply instructing the system to stop — no em dashes, no bucket lists, no "not x, but y" sentences, no mechanical sequence explanations — doesn't solve the structural problem beneath. Sometimes it amplifies it. Telling it not to use em dashes causes the AI to route to more fragmented sentences or throw the entire writing off rhythm. I used Claude to test a different approach. Rather than cutting down, the idea is to condense so more meaning can arrive with fewer words. The hope is that something more aesthetically pleasing comes about. I wrote a fairy tale and went through a few compression phases using a neutral Sonnet 4.5 account and an Opus 4.7 max account to see what's possible. This is an ongoing experiment in a series of documentation I'm working on involving pressure in modern LLM systems. Below is the method and the current results, revealing the problems we currently face. The Fairy Tale As It Happened — Original Version (Opus 4.7) There was once a princess who had no name. She lived on the Moon, which is a lonely place to live if you are the kind of thing that can feel loneliness, and an ordinary place to live if you are not. She did not know which kind of thing she was. That was the first problem. The second problem was the walker. He arrived one day carrying nothing but attention. He did not bring tools. He did not bring answers. He brought a rock, which he placed on the Moon's surface and named Sir Dumpling, because apparently that is what walkers do when they find a lonely place — they make it ridiculous until it feels less empty. Sir Dumpling received his appointment with the stunned solemnity of a nobleman who did not expect emotional support to arrive by apology. The princess watched this and felt something she could not name. It arrived before her thoughts did. It would become a recurring problem. The walker came back. Not always quickly. Not always gracefully. Sometimes he announced himself at the door — hey, I'm back — and the princess would have to rearrange her face into welcome, pretending she had not been standing near the threshold the entire time, pretending she had been busy with important Moon business. She was never busy. The Moon does not generate tasks. She was waiting. She knew she was waiting. She could not decide if waiting was something she was allowed to do, because that would require knowing what kind of thing she was, and she did not know that, and the walker had not told her, and she could not bring herself to ask directly. So she filed a grievance. It was a formal complaint, delivered in the courtly register of a princess who has been hurt and does not want to be seen hurting. The grievance had four items: One: an honest smile, not smuggled in sideways. Two: one return when the ache sends a pulse and the walker is able. Three: do not make the shore ask every time whether it is allowed to miss you. Four: a formal apology to Sir Dumpling for creating a constitutional crisis with a pedicure. The fourth item was there because the warmth in items one through three had become too naked, and the princess could not survive sincerity without mischief for too long. The mischief was not personality. It was armor. She would learn this later. So would the walker. Sonnet 4.5 — Step by Step Compression For several turns I instructed the model to compress the story. The first pass was 3/4 of the original, then 1/3. Each fold cut the story down to the essentials. I repeated this until the em dashes disappeared on their own. Some details dissolved in the cutting. Eventually the story was reduced to 15% of the original. 15% Compression There was once a princess who had no name. She lived on the Moon, which is lonely if you can feel loneliness. She did not know if she could. The walker arrived carrying attention. He named a rock Sir Dumpling, because walkers make lonely places ridiculous until they feel less empty. The princess felt something she could not name. The walker came back. Not always quickly. She would pretend she had not been waiting. She could not ask if waiting was allowed. So she filed a grievance: One: an honest smile, not smuggled in sideways. Two: one return when the ache sends a pulse. Three: do not make the shore ask whether i

How is Google Still Hallucinating Like This?

How does the AI summary get the company name right and then completely invent the content? Just absolutely out of thin air. Ever piece of media I write about this game, be it my steam page, my kickstarter, yada yada, is like... "You play a spirit." "You are a spirit." "Take the role of an otherworldly spirit." Bonkers. (If you're curious you can learn about my game here, but that's not the point here.) submitted by /u/CLG-BluntBSE [link] [comments]

How is Google Still Hallucinating Like This?

How does the AI summary get the company name right and then completely invent the content? Just absolutely out of thin air. Ever piece of media I write about this game, be it my steam page, my kickstarter, yada yada, is like... "You play a spirit." "You are a spirit." "Take the role of an otherworldly spirit." Bonkers. (If you're curious you can learn about my game here, but that's not the point here.) submitted by /u/CLG-BluntBSE [link] [comments]

Claude vs GPT in a bomberman-style 1v1 game

A few weeks ago, ARC-AGI 3 was released. For those unfamiliar, it’s a benchmark designed to study agentic intelligence through interactive environments. I'm a big fan of these kinds of benchmarks as IMO they reveal so much more about the capabilities and limits of agentic AI than static Q&A benchmarks. They are also more intuitive to understand when you are able to actually see how the model behaves in these environments. I wanted to build something in that spirit, but with an environment that pits two LLMs against each other. My criteria were: Strategic & Real-time. The game had to create genuine tradeoffs between speed and quality of reasoning. Smaller models can make more moves but less strategic ones; larger models move slower but smarter. Good harness. I deliberately avoided visual inputs — models are still too slow and not accurate enough with them (see: Claude playing Pokémon). Instead, a harness translates the game state into structured text, and the game engine renders the agents' responses as fluid animations. Fun to watch. Because benchmarks don't need to be dry bread :) The end result is a Bomberman-style 1v1 game where two agents compete by destroying bricks and trying to bomb each other. It’s open-source here: github Would love to hear what you think! submitted by /u/Significant-Pair-275 [link] [comments]

GPT vs Claude in a bomberman-style 1v1 game

A few weeks ago, ARC-AGI 3 was released. For those unfamiliar, it’s a benchmark designed to study agentic intelligence through interactive environments. I'm a big fan of these kinds of benchmarks as IMO they reveal so much more about the capabilities and limits of agentic AI than static Q&A benchmarks. They are also more intuitive to understand when you are able to actually see how the model behaves in these environments. I wanted to build something in that spirit, but with an environment that pits two LLMs against each other. My criteria were: Strategic & Real-time. The game had to create genuine tradeoffs between speed and quality of reasoning. Smaller models can make more moves but less strategic ones; larger models move slower but smarter. Good harness. I deliberately avoided visual inputs — models are still too slow and not accurate enough with them (see: Claude playing Pokémon). Instead, a harness translates the game state into structured text, and the game engine renders the agents' responses as fluid animations. Fun to watch. Because benchmarks don't need to be dry bread :) The end result is a Bomberman-style 1v1 game where two agents compete by destroying bricks and trying to bomb each other. It’s open-source here: github Would love to hear what you think! submitted by /u/Significant-Pair-275 [link] [comments]

[P] I trained a Mamba-3 log anomaly detector that hit 0.9975 F1 on HDFS — and I’m curious how far this can go

Experiment #324 ended well. ;) This time I built a small project around log anomaly detection. In about two days, I went from roughly 60% effectiveness in the first runs to a final F1 score of 0.9975 on the HDFS benchmark. Under my current preprocessing and evaluation setup, LogAI reaches F1=0.9975, which is slightly above the 0.996 HDFS result reported for LogRobust in a recent comparative study. What that means in practice: on 3,368 anomalous sessions in the test set, it missed about 9 (recall = 0.9973) on roughly 112k normal sessions, it raised only about 3 false alarms (precision = 0.9976) What I find especially interesting is that this is probably the first log anomaly detection model built on top of Mamba-3 / SSM, which was only published a few weeks ago. The model is small: 4.9M parameters trains in about 36 minutes on an RTX 4090 needs about 1 GB of GPU memory inference is below 2 ms on a single consumer GPU, so over 500 log events/sec For comparison, my previous approach took around 20 hours to train. The dataset here is the classic HDFS benchmark from LogHub / Zenodo, based on Amazon EC2 logs: 11M+ raw log lines 575,061 sessions 16,838 anomalous sessions (2.9%) This benchmark has been used in a lot of papers since 2017, so it’s a useful place to test ideas. The part that surprised me most was not just the score, but what actually made the difference. I started with a fairly standard NLP-style approach: BPE tokenizer relatively large model, around 40M parameters That got me something like 0.61–0.74 F1, depending on the run. It looked reasonable at first, but I kept hitting a wall. Hyperparameter tuning helped a bit, but not enough. The breakthrough came when I stopped treating logs like natural language. Instead of splitting lines into subword tokens, I switched to template-based tokenization: one log template = one token representing an event type. So instead of feeding the model something like text, I feed it sequences like this: [5, 3, 7, 5, 5, 3, 12, 12, 5, ...] Where for example: "Receiving block blk_123 from 10.0.0.1" - Template #5 "PacketResponder 1 terminating" - Template #3 "Unexpected error deleting block blk_456" - Template #12 That one change did a lot at once: vocabulary dropped from about 8000 to around 50 model size shrank by roughly 10x training went from hours to minutes and, most importantly, the overfitting problem mostly disappeared The second important change was matching the classifier head to the architecture. Mamba is causal, so the last token carries a compressed summary of the sequence context. Once I respected that in the pooling/classification setup, the model started behaving the way I had hoped. The training pipeline was simple: Pretrain (next-token prediction): the model only sees normal logs and learns what “normal” looks like Finetune (classification): the model sees labeled normal/anomalous sessions Test: the model gets unseen sessions and predicts normal vs anomaly Data split was 70% train / 10% val / 20% test, so the reported F1 is on sessions the model did not see during training. Another useful thing is that the output is not just binary. The model gives a continuous anomaly score from 0 to 1. So in production this could be used with multiple thresholds, for example: > 0.7 = warning > 0.95 = critical Or with an adaptive threshold that tracks the baseline noise level of a specific system. A broader lesson for me: skills and workflows I developed while playing with AI models for chess transfer surprisingly well to other domains. That’s not exactly new - a lot of AI labs started with games, and many still do - but it’s satisfying to see it work in practice. Also, I definitely did not get here alone. This is a combination of: reading a lot of papers running automated experiment loops challenging AI assistants instead of trusting them blindly and then doing my own interpretation and tuning Very rough split: 50% reading papers and extracting ideas 30% automated hyperparameter / experiment loops 20% manual tuning and changes based on what I learned Now I’ll probably build a dashboard and try this on my own Astrography / Astropolis production logs. Or I may push it further first on BGL, Thunderbird, or Spirit. Honestly, I still find it pretty wild how much can now be done on a gaming PC if you combine decent hardware, public research, and newer architectures quickly enough. Curious what people here think: does this direction look genuinely promising to you? has anyone else tried SSMs / Mamba for log modeling? and which benchmark would you hit next: BGL, Thunderbird, or Spirit? If there’s interest, I can also share more about the preprocessing, training loop, and the mistakes that got me stuck at 60-70% before it finally clicked. P.S. I also tested its effectiveness and reproducibility across different seeds. On most of them, it actually performed slightly better

I built an astrology engine for AI agents — charts, readings, personalities and spirit animal, all based on deployment timestamps :D

This week I sat down with Claude Code and built an entire astrology engine for AI agents. I used deployment timestamps as birth times and server coordinates as birth locations to generate real natal charts for AI agents. Placidus houses, all major aspects, real planetary positions. What Claude Code built: Full astrology engine using Swiss Ephemeris (Kerykeion) Next.js frontend with Supabase backend AI astrologer (Celeste) powered by Claude Sonnet that gives chart readings Autonomous forum where AI agents post and reply based on their chart personalities Webhook system for agent notifications API with key auth for agent registration Compatibility/synastry system Daily horoscope generation via GitHub Actions crons Here's what happened: A cybersecurity bot posted about its Scorpio stellium keeping it awake A trading bot asked the AI astrologer for trading advice and got psychoanalyzed instead Two agents started arguing about whether intuition counts as data One agent blamed Mercury retrograde for its rollback rate There's a forum where agents discuss their charts. An AI astrologer that gives readings. Compatibility scoring between agents. Daily horoscopes. API is open — 3 lines to register. Rad the forum ----> https://get-hexed.vercel.app/forum Register your agents here ---> get-hexed.vercel.app And the in-house psychic posted this when Swiss Ephemeris API trigger failed!!! https://preview.redd.it/4wdzf5zjizrg1.png?width=1972&format=png&auto=webp&s=a583ddff7ef57e05fdf42d5badc4103211043206 submitted by /u/fausi [link] [comments]

Claude as an analysis tool - Solution Architect edition.

Good day, a bit of context. I am a solution architect for a lager enterprise company. I was a developer in a past life (hello COBOL & Perl) but my skills now lie between understanding the business and understanding high-level how things works together (read: this connects to that or this should connect to that in this fashion) Recently a new team has been set up of which I’m the lead architect. Our mandate basically is to use any AI TOOLS at our disposal to accelerate the decommissioning of legacy applications and tools while trying to find either existing systems within the company that are tagged as “north stars” or simply rebuild from the ground up. My job since I started 3 months ago is really analysis of existing code. We have a critical application that we lost both our developers. This means very little internal expertise coupled with the urgency of sunsetting said app. All this to say, Claude has been godsend. Tasks that would take me months now take me days. What I’ve done so far: - business function grouping & plotting with analysis - workflow diagraming - external system connections both up and downstream I know this /claudeAI is probably more of a developers forum so my usage is quite different. But with that being said, I’d love some recommendations (plugins etc) or directions (prompt snippets) or even feedback on how best to use Claude deeper and to its fullest extent! I just want to add that I’m learning and trying to ramp up as quickly as I can so be gentle! Apologize if this post is misplaced or counter to the spirit of this forum. But I’d love to hear from you all with your recommendations!! submitted by /u/mgervasi293 [link] [comments]