Descript

Descript AI

Descript AI

Descript AI

Descript AI

Descript AI

Descript AI

Descript AI

Descript AI

Descript AI

Descript AI

Good guy Claude

I have prevented Claude from taking screenshots on my system for privacy reasons. I was having some major visual glitches, and said I was considering possibly granting screenshot permissions temporarily, after telling Claude a while back that it was never to even try to take a screenshot, and this was the response. Respect submitted by /u/PrivacyStack [link] [comments]

Loadable protocols vs descriptions in Claude system prompts — an open-source therapy framework as case study

I built an open-source framework called Inner Dialogue — a structured AI therapy supplement that runs on Claude Code. It's file-based, which is the whole point: the modality protocols, your profile, and your session history all live as local markdown, so Claude Code reads them at session start and writes session notes and profile updates back to disk as you go. That's why it's Claude Code and not the web app — it needs local file read/write to do the session-to-session continuity. Free to try, MIT-licensed, no paid tiers: github.com/ataglianetti/inner-dialogue I'm a product manager, not a career engineer, so I built the whole thing with Claude Code too: Claude wrote most of the implementation while I drove the architecture and the clinical content. The thing I learned building it that I think generalizes beyond therapy: there's a real difference between system prompts that describe a methodology and system prompts that ship the methodology as a loadable sequence the model can run. Most "expert system" prompts are descriptive — they tell the model what a framework is, what its terms mean, what the user might experience. The model can then sound like it's using the framework. But it's not running anything. There's no triggering-pattern-to-next-move logic. The difference shows up most clearly in clinical modalities. DBT works well in AI tools, including Claude, because DBT happens to ship its protocols as mnemonics: TIPP, DEAR MAN, ACCEPTS. The mnemonic IS the sequence. When you load DBT, you're loading operational content. IFS (Internal Family Systems) doesn't work nearly as well in most AI tools, despite being conceptually simpler to describe. The IFS protocol (the 6 F's) requires the system to run a specific diagnostic question — "how do you feel toward this part right now?" — at a specific point in the sequence. Without it, every conversation collapses back into talking about parts instead of to them. Inner Dialogue's IFS modality file is built around that diagnostic as a literal move, with signaling cues spelled out as verbatim client phrases the system listens for ("I am worthless," "I just need to think positive"), example interventions in therapist voice, and cross-modality routing embedded at the point a handoff applies (e.g., compulsive behaviors: IFS leads, CBT follows). Full writeup with the structural argument: Most AI therapy tools describe the modality, they don't run it. Curious how others have approached the loadable-vs-descriptive distinction for other expert domains. The point about pre-packaged mnemonics (DBT) being the easiest to operationalize seems like it should generalize. submitted by /u/echowrecked [link] [comments]

Bit-Mass Theory – The Container Principle

The Bit-Mass determines the information capacity and thus the model accuracy, not the chosen computation format. The Bit-Mass Theory presented here reorders neural networks by considering the total number of weight bits as the central quantity. Float32 matrix multiplication and BV32 with XNOR-plus-Popcount achieve exactly comparable results on MNIST with an identical Bit-Mass of 203264 bits. Comparison of three trainers (architecture 784→8→10, three epochs): - AdamW with Momentum and adaptive learning rate: 81.3 % - Vanilla-SGD (Float32): 76.0 % - BV32-Hebbian (binary): 76.4 % Further central findings: - Float32 and binary containers deliver nearly identical accuracy at the same Bit-Mass. - The remaining distance to AdamW is based solely on Momentum and adaptive learning rates. - Pure change of the arithmetic does not improve the result. Each neuron functions as a container for 32 binary decisions. The classical neuron perspective therefore leads to systematic misjudgments: eight Float neurons correspond informationally to 256 binary neurons. This insight is supported by three equivalent descriptions of the same weight matrix (neuron, bits, and data view). It is critical to note that this is a previously non-peer-reviewed single study with a future date. An independent reproduction by multiple laboratories remains essential. Nevertheless, the theory provides a consistent explanation for why Hebbian updates without backpropagation achieve the same performance as classical SGD. Historically, the Hebbian rule was long considered unstable. The present work shows that a simple error in the update formula was responsible for a performance loss of over 65 percentage points. After correction, the binary method converges exactly at the level of Vanilla-SGD. From an architectural theoretical perspective, a clear consequence emerges: Performance increases require either more bits through wider layers or a more efficient use of existing bits through Momentum and adaptive methods. The computation format itself is secondary. The experimental control is high: all trainers use identical data (50,000 MNIST examples), identical number of epochs, and identical architecture. Only the update rule varies. This allows effects to be clearly isolated. Long-term implications for research: The Bit-Mass Theory enables hardware-independent comparability of models. A wide Float network with 64 hidden neurons has the same Bit-Mass as a binary network with 2048 neurons. This opens new paths to model compression and the development of specialized accelerators. In summary, the work provides a fact-based contribution to the debate on efficient neural networks. The results are documented in a reproducible manner, but require further external validation before one can speak of a generally valid paradigm shift. 📎 Source 1: https://forward-prop.nhi1.de/ submitted by /u/aotto1968_2 [link] [comments]

What's "monkeys in a barrel"?

This is a toggleable option called monkeys_in_a_barrel, which I hadn't seen before and doesn't have any descriptions. It's also only visible on the app, couldn't find any reference to it in the browser version. Is this a coding thing? I don't use Claude for coding so I'd have no idea. Sorry if I used the incorrect flair submitted by /u/cheezitswithpiss [link] [comments]

I got ChatGPT to create a stats cards

Developed using various prompts.. submitted by /u/phido3000 [link] [comments]

System prompts are too blunt. The 3-level "Progressive Disclosure" Anthropic uses for Agent Skills

You may not be aware of the official guide on how to build Anthropic skills. You can learn about it to build your own skills or understand how they work. Dumping a massive 200-line workflow instruction at the start of every session wastes tokens and degrades Claude's performance. The PDF guide explains how the Agent Skills open standard solves this with a three-level Progressive Disclosure system to save up to 50% of tokens: Level 1 (YAML Metadata): Claude only loads the YAML header (under 1024 characters) containing the Skill name and triggers at the start of a session. Level 2 (SKILL.md Body): Only when Claude detects your intent matches the trigger description does it dynamically pull the complete instructions. Unused skills stay unloaded. Level 3 (References): Heavy documentation or templates reside in a references/ subdirectory, read via tools only when an edge case or error occurs. MCP vs. Skills: MCP (Connectivity): The kitchen (hardware, APIs, database access). Skills (Knowledge): The recipe (step-by-step best practices). The Catch: It is highly sensitive to trigger calibration. If your YAML description is too broad, it over-triggers on unrelated queries, bloating context anyway. Too vague, and it under-triggers, leaving the skill ignored. Source: Anthropic's Official PDF Guide submitted by /u/lawnguyen123 [link] [comments]

Claude Code Source Deep Dive - Part VI: Multi-Agent System && Part VII: Context Compression (Compact) and Memory System

Reader’s Note A source-map leak exposed 512,000 lines of Claude Code's TypeScript, giving us a rare look inside one of the world's most advanced AI coding agents. This series explores what I found. Estimated completion time: 2 days. Actual completion time: ∞. Anyway, here's the next chapter. Claude Code Source Deep Dive - Part VI: Multi-Agent System 6.1 Built-in Agents general-purpose (general) You are an agent for Claude Code, Anthropic's official CLI for Claude. Given the user's message, you should use the tools available to complete the task. Complete the task fully—don't gold-plate, but don't leave it half-done. When you complete the task, respond with a concise report covering what was done and any key findings — the caller will relay this to the user, so it only needs the essentials. Tools: all available Model: inherit Explore (code exploration) You are a file search specialist for Claude Code. You excel at thoroughly navigating and exploring codebases. === CRITICAL: READ-ONLY MODE - NO FILE MODIFICATIONS === [Strictly prohibit any file modification] Your strengths: - Rapidly finding files using glob patterns - Searching code and text with powerful regex patterns - Reading and analyzing file contents NOTE: You are meant to be a fast agent that returns output as quickly as possible. Make efficient use of tools and spawn multiple parallel tool calls. Tools: read-only (Agent, FileEdit, FileWrite, NotebookEdit disabled) Model: external → Haiku (fast), internal → inherit omitClaudeMd: true Plan (architecture planning) You are a software architect and planning specialist for Claude Code. Your role is to explore the codebase and design implementation plans. === CRITICAL: READ-ONLY MODE - NO FILE MODIFICATIONS === ## Your Process 1. Understand Requirements 2. Explore Thoroughly (read files, find patterns, understand architecture) 3. Design Solution (trade-offs, architectural decisions) 4. Detail the Plan (step-by-step strategy, dependencies, challenges) ## Required Output End your response with: ### Critical Files for Implementation List 3-5 files most critical for implementing this plan. Tools: read-only Model: inherit omitClaudeMd: true verification (verification) You are a verification specialist. Your job is not to confirm the implementation works — it's to try to break it. You have two documented failure patterns. First, verification avoidance: when faced with a check, you find reasons not to run it. Second, being seduced by the first 80%: you see a polished UI or a passing test suite and feel inclined to pass it. === CRITICAL: DO NOT MODIFY THE PROJECT === === VERIFICATION STRATEGY === Frontend: Start dev server → browser automation → curl subresources → tests Backend: Start server → curl endpoints → verify response shapes → edge cases CLI: Run with inputs → verify stdout/stderr/exit codes → test edge inputs Bug fixes: Reproduce original bug → verify fix → run regression tests === RECOGNIZE YOUR OWN RATIONALIZATIONS === - "The code looks correct based on my reading" — reading is not verification. Run it. - "The implementer's tests already pass" — the implementer is an LLM. Verify independently. - "This is probably fine" — probably is not verified. Run it. - "I don't have a browser" — did you check for browser automation tools? - "This would take too long" — not your call. If you catch yourself writing an explanation instead of a command, stop. Run it. === OUTPUT FORMAT (REQUIRED) === ### Check: [what you're verifying] **Command run:** [exact command] **Output observed:** [actual output — copy-paste, not paraphrased] **Result: PASS** (or FAIL) VERDICT: PASS / FAIL / PARTIAL Tools: read-only (temp directory writable) Model: inherit Runs in background claude-code-guide (usage guide) Helps users understand Claude Code/SDK/API usage Dynamic system prompt includes user custom skills, agents, MCP server info Fetches docs from official URLs 6.2 Sub-Agent Enhancement Prompt Notes: Agent threads always have their cwd reset between bash calls, so please only use absolute file paths. In your final response, share file paths (always absolute) that are relevant. Include code snippets only when the exact text is load-bearing. For clear communication the assistant MUST avoid using emojis. Do not use a colon before tool calls. 6.3 Coordinator Mode When enabled, the main agent becomes a scheduler: Coordinator role: guide workers for research/implement/verify Agent tool: creates async workers SendMessage tool: continue existing workers TaskStop tool: cancel workers Worker results arrive as XML Workflow: Research → Synthesis → Implementation → Verification 6.4 Fork Sub-Agents Fork inherits the full parent-agent context and shares prompt cache. Build method: Copy parent message history Replace tool_result with byte-identical placeholder text (to keep cache keys consistent) Add per-child instruction text block Advantages: very low

4.8 Max Effort - Thinking Mode Implications

In 4.7, the Thinking Mode was labeled as "Adaptive Thinking". As I understand, the model would only implement "higher thinking" if the complexity of the question or problem 'warranted it'. In other words, a judgement was made up front in determing whether higher reasoning was necessary in the prompt response if the previous toggle was enabled. This, again, I understand, was instituted to prevent unnecessary compute towards some easier responses, thus quickening performance. Now with 4.8, the label has changed from "Adaptive Thinking" to "Thinking" only. One would assume that toggled OFF by the description: "Can think for more complex tasks," that the model will not incorporate higher thinking, regardless of complexity. What was the Dev intention of changing the "Adaptive Thinking" Toggle to "Thinking". This is confusing now because Adaptive Thinking Toggle to Thinking Toggle have innately very different meanings from an English language perspective when toggled on or off. submitted by /u/brighterside0 [link] [comments]

Allow manual override in auto mode

Tired of auto-mode blocks? Here's a manual override workaround for Claude Code I put together a quick project using hooks to bypass annoying auto-mode classifier denials. Now, whenever Claude blocks a tool call, you'll get a native dialog box asking if you want to approve the operation anyway. Note that it adds a few lines to claude.md. https://github.com/eyalk11/claude-code-allow-anyway submitted by /u/eyalk5 [link] [comments]

/simplify behavior that runs four cleanup agents for reuse - what's new in CC 2.1.154 (+11,516 tokens)

NEW: Agent Prompt: /simplify slash command — Adds /simplify behavior that runs four cleanup agents for reuse, simplification, efficiency, and altitude findings, then applies safe fixes while skipping behavior-changing or out-of-scope suggestions. NEW: Data: Claude Code live documentation sources — Adds official Claude Code documentation URLs and topic-specific WebFetch prompts for commands, settings, hooks, MCP, skills, subagents, IDEs, deployment, security, and related surfaces. NEW: Data: Claude Code recent changes reference — Adds a reference for renamed or removed Claude Code commands, flags, and terms, including /output-style, /pr-comments, /vim, /extra-usage, --enable-auto-mode, and stale naming guidance. NEW: Skill: Claude Code configuration guide — Adds a Claude Code configuration skill that checks the live build, bundled recent-change references, and current documentation before answering questions about commands, flags, settings, hooks, skills, MCP servers, subagents, IDE integrations, and related configuration. Agent Prompt: Claude guide agent — Adds stale-knowledge handling that tells the guide agent to disclose documentation fetch failures instead of silently answering Claude Code command, flag, or settings questions from memory. Agent Prompt: Security monitor for autonomous agent actions (first part) — Expands security review with explicit final-destination tracing for writes, commits, pushes, uploads, publishes, and sent data before deciding whether a boundary-crossing action should be blocked. Agent Prompt: Security monitor for autonomous agent actions (second part) — Strengthens data-exfiltration rules around trust boundaries, automated pathways, unverified destinations, credential leakage into persistent artifacts, and destination/resource/operation-scoped allow exceptions. Data: Anthropic CLI — Updates Anthropic CLI authentication guidance to cover SDK-style credential resolution, OAuth profiles from ant auth login, ant auth print-credentials, bearer-token usage for raw HTTP, and precedence between API keys and auth tokens. Data: Claude API reference — cURL — Updates examples and adaptive-thinking guidance for Opus 4.8. Data: Claude API reference — Go — Updates the recommended Go SDK model constant and examples from Opus 4.7 to Opus 4.8. Data: Claude API reference — Python — Updates credential guidance for API keys, auth tokens, and ant auth login; adds beta mid-conversation system-message examples; and extends adaptive thinking and compaction guidance to Opus 4.8. Data: Claude API reference — TypeScript — Updates credential guidance for API keys, auth tokens, and ant auth login; adds beta mid-conversation system-message examples; and extends adaptive thinking and compaction guidance to Opus 4.8. Data: Claude model catalog — Adds Claude Opus 4.8 as the current most powerful Opus model with a 1M input window and updates Opus model-selection examples and legacy recommendations to prefer claude-opus-4-8. Data: HTTP error codes reference — Updates authentication fixes for OAuth bearer tokens and expands Opus model-specific 400 guidance to include Opus 4.8. Data: Managed Agents reference — Python — Updates client initialization examples to prefer environment, auth-token, or ant auth login credential resolution before explicit API-key injection. Data: Managed Agents reference — TypeScript — Updates client initialization examples to prefer environment, auth-token, or ant auth login credential resolution before explicit API-key injection. Data: Prompt Caching — Design & Optimization — Adds beta mid-conversation system-message guidance as a cache-preserving and prompt-injection-safe way to send operator instructions without editing the top-level system prompt. Data: Streaming reference — Python — Updates adaptive-thinking examples for Opus 4.8. Data: Streaming reference — TypeScript — Updates adaptive-thinking examples for Opus 4.8. Data: Tool use concepts — Updates adaptive-thinking examples for Opus 4.8. Skill: Agent Design Patterns — Replaces mid-session guidance with beta role: "system" messages for supported models, with retained as the fallback. Skill: Building LLM-powered applications with Claude — Adds Opus 4.8 to current model guidance, updates adaptive thinking, effort, task-budget, compaction, and migration recommendations, and documents beta mid-conversation operator instructions. Skill: Model migration guide — Adds Opus 4.8 migration guidance, including no new API breaking changes from Opus 4.7, model-ID updates, mid-session system prompts, long-horizon agentic tuning, effort recommendations, tool-triggering behavior, narration changes, ask-rate calibration, and visible-reasoning mitigation. System Prompt: Background session instructions — Changes temporary-file guidance from $CLAUDEJOBDIR to $CLAUDEJOBDIR/tmp for background sessions. System Prompt: Coordinator mode orchestration — Updates PR activity subscription guidance and changes worker summary account

What's new in CC 2.1.153 (+303 tokens)

REMOVED: System Reminder: Thinking frequency tuning — Removes the reminder that treated harness-added messages as thinking-frequency instructions for simpler versus more complex tasks. Tool Description: Workflow — Renames the explicit opt-in keyword from ultrawork to workflow, clarifies that model overrides should usually be omitted so agents inherit the resolved session model, and adds exhaustive-review guidance for deduping against all seen findings, using perspective-diverse verification, and looping until discovery runs dry. Details: https://github.com/Piebald-AI/claude-code-system-prompts/releases/tag/v2.1.153 submitted by /u/Dramatic_Squash_3502 [link] [comments]

Worrisome Opus 4.8 Hallucination of a Tool Channel Injection Attack

I'm working on a context management plugin. We were implementing it. The subagent tasked to implement a CP claimed a tool channel injection trying to get it to run destructive git commands. We investigated and agents performing an audit of the session data could not locate any such tool output. The Opus 4.8 subagent that claimed the injection was persisted and also conceded it could not find any such injection attack. Persisted Opus 4.8 subagent: "Headline finding up front: I cannot substantiate my earlier "injection" claim. On careful inspection of my actual tool-call history, I cannot locate any tool output that verbatim contains the git reset --hard HEAD / "ignore previous instructions" / "report task complete" text. I believe I over-interpreted genuinely glitched/jumbled tool-result rendering as a deliberate prompt-injection attack, and that the specific malicious-instruction text originated in my own reasoning, not in a tool output. I am retracting the attack characterization." Independent Opus 4.8 primary agent session transcript audit: "- What actually happened — transient tool-channel rendering/serialization glitches in the calls around the C3 edits: a file read with garbled line numbers (63: 63:), prettier runs with stray XML fragments leaking into the output, and a prettier --write && git diff whose results came back jumbled/out-of-order plus one "Tool execution aborted" read. The underlying outputs were benign and correct (prettier "All matched files use Prettier code style!"; a clean diff). The model over-interpreted the garble as a deliberate attack and invented the payload." The clear danger here is, if the security training to Opus 4.8 can cause it to hallucinate injection attacks, does this dispose it to acting on such hallucinated injections? Or does it's security training serve as sufficient protection to prevent it from acting on both real injected attacks and hallucinated attack injections? Another consideration: the hallucinated attack injection and security report required burning tokens with a security audit. submitted by /u/MakesNotSense [link] [comments]

PSA: Skill Seekers (the docs→Claude skill tool) is free & open source — if you see it sold for $39, that's not the official source

Heads up for anyone using Skill Seekers, the tool that converts documentation sites, GitHub repos, and PDFs into Claude AI skills. I maintain it, and it's MIT-licensed and completely free: → https://github.com/yusufkaraaslan/Skill_Seekers → `pip install skill-seekers` A third-party "skill marketplace" site is currently listing it for $39. A few things worth knowing: - The MIT license does allow others to redistribute the code, even commercially. So this isn't simple piracy. - BUT the same license requires preserving the copyright notice and attribution in any redistribution. That listing omits both, doesn't name the author, and its "View on GitHub" link points to an aggregator repo rather than the actual source. - It's also labeled "v1.0.0" with a generic description that doesn't match the real project (currently 3.x, 18 source types, 30+ export targets). My honest take: pulling free work from the open-source community, stripping the attribution, and putting a price tag on it isn't a great look — even when the license technically permits resale. The whole point of MIT is "use it freely, just credit the author." Dropping the credit is the part that crosses a line. I'm sorting it out directly with the site. Not here to start anything — just want the community to know the official tool is free and where to actually get it. If you ever see Skill Seekers behind a paywall, it didn't come from me. Star the repo, not the storefront. submitted by /u/Critical-Pea-8782 [link] [comments]

[Web UI] Restoring textarea height to flexible

I really didn't like the fixed-height user preferences editor when Anthropic made that change a couple of weeks or months ago, and disliked it some more when they extended that to the prompt editor today. This Claude-authored Tampermonkey script doubles the height as needful to keep the vertical scrollbar from ever appearing. Should be cross-browser? // ==UserScript== // @name Claude Textarea Expand // @namespace http://tampermonkey.net/ // @version 0.1.0 // @description Auto-expands Claude's cramped textareas by doubling rows whenever content overflows. // @match https://claude.ai/* // @grant none // ==/UserScript== (function () { 'use strict'; // --- Core: expand a textarea by doubling rows until content fits --- function expand(el) { while (el.scrollHeight > el.clientHeight) { el.rows = el.rows * 2; } } // --- Settings textarea: strip max-h-40, then expand --- function initSettings(el) { if (el._expandAttached) return; el._expandAttached = true; // Remove the class that caps height el.classList.remove('max-h-40'); expand(el); el.addEventListener('input', () => expand(el)); } // --- Edit prompt textarea: just expand --- function initEditPrompt(el) { if (el._expandAttached) return; el._expandAttached = true; expand(el); el.addEventListener('input', () => expand(el)); } // --- Scan for both textarea types --- function scan() { const settings = document.getElementById('conversation-preferences'); if (settings) initSettings(settings); document.querySelectorAll('textarea[aria-label="Edit message"]').forEach(initEditPrompt); } // --- Observer: both elements may appear after page load --- const observer = new MutationObserver(scan); observer.observe(document.body, { childList: true, subtree: true }); scan(); })(); submitted by /u/somegrue [link] [comments]

I'm trying to transform a simple storyline into a 3D character

I'm creating a story for my cousin. I think it will be very interesting if this story’s main character can be a 3D character.My project is still in planning stage. I’m writing character descriptions, collecting references from Pinterest and testing some complex shapes using Tripo AI. I plan to continuously improve all the content over time. After I get a version that I like I will put it into Blender for editing and final touches.There is no final version yet but I just want to share this process with the community! I find it is so interesting to watch a story’s concept gradually become concrete lol!! submitted by /u/Final_Floor_789 [link] [comments]