Mutable AI

Mutable AI AI

Mutable AI AI

Mutable AI AI

Mutable AI AI

Mutable AI AI

Mutable AI AI

Mutable AI AI

Mutable AI AI

Mutable AI AI

Mutable AI AI

Sometimes the obvious...is not so obvious.

C.C., old buddy, why did you write 50 lines of code to ensure a constant wasn't mutable?" I love Opus, man. "He" reminds me of an old friend who was absolutely brilliant, but give him too many bong hits and he was off in a rabbit hole talking about UFOs, fifth dimensional travel and, "Bob Lazar is full of shit, man!" The mods wanted me to provide the 50 line sample that backs up my opening quote (rightfully so.) It happened with work code, so I can't copypasta, but that little ditty went something like this: (insert slow jazz here) 1 import inspect import sys import logging class ImmutableConstantMeta(type): """Metaclass to prevent rebinding of class-level constants.""" def __setattr__(cls, name, value): if name.isupper(): raise TypeError(f"CRITICAL: Cannot rebind constant '{name}'") super().__setattr__(name, value) class LegacyMigrationConfig(metaclass=ImmutableConstantMeta): # The actual constant that should have just been 1 line MAX_DB_RETRIES = 3 def max_db_retries(self): """Getter to ensure the constant is accessed safely.""" # Sanity check the constant's type in memory if not isinstance(self.MAX_DB_RETRIES, int): logging.critical("Security Alert: Constant type mutated in memory!") raise ValueError("MAX_DB_RETRIES must be an integer.") # Sanity check the value bounds if self.MAX_DB_RETRIES 10: logging.critical("Integrity Error: Constant bounds violated!") raise ValueError("MAX_DB_RETRIES must be between 0 and 10.") # Inspect the calling frame to ensure authorization caller_frame = inspect.currentframe().f_back caller_module = inspect.getmodule(caller_frame) if caller_module is not None and "django" not in caller_module.__name__ and "scripts" not in caller_module.__name__: logging.warning(f"Suspicious access from {caller_module.__name__}") # Ensure the integer memory signature hasn't changed unexpectedly if sys.getsizeof(self.MAX_DB_RETRIES) > 28: raise MemoryError("Constant memory allocation altered by external process.") return self.MAX_DB_RETRIES .setter def max_db_retries(self, value): """Strictly block any assignment attempts with a hard exception.""" logging.error(f"Attempted mutation of MAX_DB_RETRIES to {value}") raise AttributeError( "Attempted to mutate a protected constant. " "MAX_DB_RETRIES is strictly immutable and locked at the metaclass level." ) u/max_db_retries.deleter def max_db_retries(self): """Strictly block any garbage collection or deletion attempts.""" raise TypeError("Cannot delete a protected system-level migration constant.") # Helper function to access the constant safely def get_safe_retry_limit(): config = LegacyMigrationConfig() return config.max_db_retries Like, dude. I'm not writing SIL 4 code in Python.2 I'm an old programmer. I was refactoring COBOL in the 90s, man. (I swear I'm not a hipster.) I absolutely love Claude Code. CC is nothing short of a miracle. I may even be able to retire early because of CC. Hell, the fact that I may even be able to retire, at all, because of AI, would be a miracle.3 So, I find the juxtaposition between "this sucks" and "this rocks" humorous. I know Louis CK is a polarizing figure, but he had one old bit that struck a nerve with me. He was on a plane and Wifi (on a plane) was new. Everyone was amazed. Shortly into the flight, the Wifi failed and some guy scoffed, "This is bullshit, man." Louis' point was the guy wasn't appreciating the fact that Wifi, on a plane, was even possible or the technological miracles mankind has achieved, in such a short period of time. (My friend would say it's because Boeing reverse-engineered that "shit" they found in Roswell.) Having said all of that, I'm grateful for this technology. It's not a perfect tool, but damn if it isn't useful most of the time. And that's good enough for me. I've encountered my share of goofiness (like the nonsense above) and maddening edits that have really pissed me off. Here are my 3 tips to get CC's best. They're not original. These are all just anecdotal and IME, so take it with a grain of sodium chloride (or sodium hydroxide, if you're nasty.) 1.) Clear early, clear often. 1m context is not real. It sounds cool. The idea is cool...but, if you cross over 250K tokens, you're going to have a bad time. 2.) CC ignores your CLAUDE.md and explicitly does something you tell "him" not to? Or "he" makes an egregious, WTF error? Exit CC and restart. Do not clear. Exit the CLI, all the way. If you're configured to get the latest release, you may just find yourself on a new version of CC that fixes the very issues you were encountering a moment ago. 4 3.) Plan. Plan to plan...and then discuss. I may spend a full day -- or even a couple of days5 -- working on a plan and then going back and forth with CC to refine it before any code is written. Think of it this way: how good of a job are you going to do assembling an Ikea armoire (Shitzfling) without the instructions? So, there you have it. My honest take and experience in working with this "miracle worker." It can be fu

9 months, 60+ cells — what I observed building with AI

I've been building a modular personal operating system on top of Claude Code for 9 months. ~60 isolated folders ("cells"), each owning one concern — text-to-speech, clipboard management, dictation, radial menu, keyboard cleaner, screenshot, GIF recording, activity tracking, and more. I run 6-8 agents daily, 8-10 hours. These are patterns I noticed over 9 months. Not rules — observations. Your mileage will vary. Heads-up: this isn't a starter guide. I'm assuming you've already been building with Claude Code (or similar) for a while. If you're just starting out, some of this may feel overwhelming — skim the headers and come back when a section clicks. For context — here's me building with a broken arm, one-handed, in Turkish: https://www.youtube.com/watch?v=Akh2RHCzab0&t=628s — not a narration of this post, just a session where some of these patterns show up in use (custom menus, voice, conv tool, invariants). The #1 thing I noticed: my input > my prompt I noticed AI doesn't follow my prompts the way I expect. What seems to happen is — AI follows ME. My brain, my real-time corrections, my navigation. I write a system prompt. My brain is in that context. I intuitively correct AI when it drifts. When I step away from that context — the prompt alone seems to fail within a few turns. I noticed this clearly when I was tired. After 8-10 hours, same system prompt, same hooks, same architecture — things started breaking. The navigation was off, the input was off. It felt like the controller was my brain, not my text. **Priority stack — what I observed matters most:** rank what what I noticed ──── ─────────────────────── ────────────────────────────────────── 1 my input brain context seemed to matter most 2 project context fractals, folder structure, existing code 3 system prompt + hooks helps, but felt less impactful than 1 and 2 4 manifest registry YAML front-matter — guessable felt better than strict 5 truth tables layer + gate — AI processes one layer at a time Fractals: AI seems to copy the nearest cell This reminded me of company culture — people sometimes copy the person next to them more than the rules document. I noticed AI doing something similar. I have ~60 folders with the same structure: Cells/{name}/ ├── MANIFEST.md ← YAML front-matter: name, platform, commands, hooks ├── product/ │ ├── engine/ ← immutable logic (switch/dispatch) │ └── runtime/ ← mutable data (seed/config/UI) └── fossil/ ← quick-access snapshots for me (git is too many hops when I need speed) When AI needs to create a new cell, I noticed it looks at the nearest existing cell and copies the pattern. No instruction needed. The convention seemed to become the instruction. (I learned later this kind of structure has a name — apparently it's called swarm architecture. I didn't set out to build one; the cell-shape just kept paying off until the system was already operating that way.) cell-browser My cell browser. 60+ folders, each with a colored icon. (1) The grid shows every cell — database, dictation, elevenlabs, speech, etc. (2) Tabs at top: Context, Logs, Commands, Transforms — for controlling the system. (3) While talking, I pick a cell and copy its context to AI. (4) Bottom tabs give different views: File Paths, Source Content, Symbols, Manifest. The MANIFEST.md registers each cell into parent cells (telegram, mac, claude) via front-matter. AI reads structured metadata instead of scanning all source code. clipboard-panel Clipboard panel. Left: searchable list of everything I copied, with timestamps. Right: rendered MANIFEST.md preview — elevenlabs cell YAML front-matter visible (type, pain, capabilities, consumer cells). This is what AI reads instead of scanning source files. What I've come to believe: **guessable + predictable felt better than strict + verbose** — for my case. Switch cases: I noticed the compiler catches more than instructions I use Swift exhaustive enums. Each state = explicit case. The compiler catches missing ones. public enum RunContext: String, CaseIterable, Sendable { case claudeCodeSession // auto-view default case claudeCodeNoSession // browse default case standalone // no Claude Code env case piped // raw output case fzfCallback // internal mechanism } conv-tool Terminal: `conv 4f7bf66f...` extracted a session — 16 turns, ~17.2k content, ~186.2k context. Token breakdown: User 1.8k (4%), Thinking 24.7k (68%), Response 5.4k (15%), Tools 2.4k (6%), Agents 1.6k (4%). Each category is a case in a Swift enum. I noticed tables seem to work better than if/else chains for me. If AI needs to handle a new case, the compiler forces it. No silent miss. I tell AI: make every state transformation obvious. When I click the record button, idle → recording. When I click stop, recording → processing. When I click cancel, recording → discarded. Every transition = explicit switch case. If I forget the context, AI can see the code and think correctly. Truth tables: every decision is a

Agentic OS — an governed multi-agent execution platform

I've been building a system where multiple AI agents execute structured work under explicit governance rules. Sharing it because the architecture might be interesting to people building multi-agent systems. What it does: You set a goal. A coordinator agent decomposes it into tasks. Specialized agents (developer, designer, QA, etc.) execute through controlled tool access, collaborate via explicit handoffs, and produce artifacts. QA agents validate outputs. Escalations surface for human approval. What's different from CrewAI/AutoGen/LangGraph: The focus isn't on the agent — it's on the governance and execution layer around the agent. Tool calls go through an MCP gateway with per-role permission checks and audit logging Zero shared mutable state between agents — collaboration through structured handoffs only Policy engine with configurable approval workflows (proceed/block/timeout-with-default) Append-only task versioning — every modification creates a new version with author and reason Built-in evaluation engine that scores tasks on quality, iterations, latency, cost, and policy compliance Agent reputation scoring with a weighted formula (QA pass rate, iteration efficiency, latency, cost, reliability) Architecture: 5 layers with strict boundaries — frontend (visualization only), API gateway (auth/RBAC), orchestration engine (24 modules), agent runtime (role-based, no direct tool access), MCP gateway (the only path to tools). Stack: React + TypeScript, FastAPI, SQLite WAL, pluggable LLM providers (OpenAI, Anthropic, Azure), MCP protocol. Configurable: Different team presets (software, marketing, custom), operating models with different governance rules, pluggable LLM backends, reusable skills, and MCP-backed integrations. please guys, I would love to get your feedback on this and tell me if this is interesting for you to use submitted by /u/ramirez_tn [link] [comments]

Agentic OS — an governed multi-agent execution platform

I've been building a system where multiple AI agents execute structured work under explicit governance rules. Sharing it because the architecture might be interesting to people building multi-agent systems. What it does: You set a goal. A coordinator agent decomposes it into tasks. Specialized agents (developer, designer, QA, etc.) execute through controlled tool access, collaborate via explicit handoffs, and produce artifacts. QA agents validate outputs. Escalations surface for human approval. What's different from CrewAI/AutoGen/LangGraph: The focus isn't on the agent — it's on the governance and execution layer around the agent. Tool calls go through an MCP gateway with per-role permission checks and audit logging Zero shared mutable state between agents — collaboration through structured handoffs only Policy engine with configurable approval workflows (proceed/block/timeout-with-default) Append-only task versioning — every modification creates a new version with author and reason Built-in evaluation engine that scores tasks on quality, iterations, latency, cost, and policy compliance Agent reputation scoring with a weighted formula (QA pass rate, iteration efficiency, latency, cost, reliability) Architecture: 5 layers with strict boundaries — frontend (visualization only), API gateway (auth/RBAC), orchestration engine (24 modules), agent runtime (role-based, no direct tool access), MCP gateway (the only path to tools). Stack: React + TypeScript, FastAPI, SQLite WAL, pluggable LLM providers (OpenAI, Anthropic, Azure), MCP protocol. Configurable: Different team presets (software, marketing, custom), operating models with different governance rules, pluggable LLM backends, reusable skills, and MCP-backed integrations. agenticompanies.com please guys, I would love to get your feedback on this and tell me if this is interesting for you to use you can register with email/passoword to view the platform but if you want to operate agentsession I need to send you an invitation code. please feel free to DM me for an invitation code you would also need to use your Anthropic or OpenAI API key to operate then engines Thanks submitted by /u/ramirez_tn [link] [comments]