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Using LLMs directly as classifiers is frustrating because they can't be calibrated, don't reliably use all available data, and are hard to improve systematically. Instead, treat LLM outputs as features in a logistic regression or other ML model, which solves these problems while keeping the LLM's signal.
- Direct LLM classification fails on three critical fronts: you can't calibrate confidence scores or adjust precision-recall tradeoffs, the model may ignore structured data or context you provide, and you have no visibility into what the LLM actually used to make its decision.
- Wrapping an LLM verdict in logistic regression automatically fixes calibration (empirically matching true probabilities), lets you add other features, and gives interpretability about how much the LLM contributes to the final decision.
- On an irony detection task, logistic regression on the raw LLM verdict improved Brier score from 0.259 to 0.175 just by calibrating; adding more LLM-extracted features (token probabilities, multiple reasoning passes) provides a clear path to further improvement that prompt-tweaking doesn't offer.
Most prompts degrade into contradictory messes because teams keep adding to them without removing anything, causing real performance hits. Treating prompts like code—structured, modular, and maintained—can unlock 30-50% improvements in speed, retention, and costs.
- Prompts grow through accretion without review, creating contradictions and hidden ambiguities that agents can't resolve; one startup cut LLM spend by $20M/year just by removing duplication
- Prompt decisions are product decisions—agent behavior shapes user experience directly, so vague specifications like "never mention competitors" fail because they lack concrete rules
- MECE structure (mutually exclusive, collectively exhaustive sections) eliminates overlap and makes prompts maintainable like modular code; separate concerns into context, behavior, and output sections
Sam Altman claims you can stop manually writing prompts and instead build systems where AI generates its own prompts. The core idea is moving from using AI as a tool to having it work autonomously for you.
- Manual prompt engineering is becoming obsolete — the next level is letting AI handle prompt generation itself
- There's a massive skill gap: most people use LLMs inefficiently, and this 38-minute explanation bridges that gap
- Building self-prompting systems is the practical difference between AI as a tool versus AI as an autonomous worker
Andrej Karpathy walks through practical daily AI workflows in a 2-hour video, covering model selection, reasoning models, code execution, and multi-chat memory — techniques most people never use. Someone extracted these methods into a Claude-specific guide with ready-to-use examples.
- Most people use only 10% of what AI models can do; this covers the remaining 90%
- Specific techniques shown include choosing the right model, deciding when reasoning models justify the cost, generating full research reports from single prompts, and automating code execution
- The guide translates Karpathy's video into Claude-specific features with immediately applicable examples
Anthropic published its internal onboarding curriculum for new hires, which teaches a four-part framework for managing AI work: deciding what to delegate, describing intent and constraints clearly, evaluating outputs critically, and taking responsibility for results. Prompting is just one quarter of one competency—the other three quarters focus on choosing the right tasks, reviewing AI output, and owning what gets shipped. Some people are now repackaging this free, publicly available material as paid courses.
- Anthropic publicly released its internal 4D onboarding framework (Delegation, Description, Discernment, Diligence) for free under Creative Commons, complete with a certificate—yet people are already reselling it as paid courses.
- Prompting is just one component of "Description," meaning it accounts for roughly 25% of one out of four competencies, not the whole picture of working effectively with AI.
- The other 75% involves judgment calls: deciding what to delegate to AI, critically evaluating its output, and taking responsibility for what ships.
- Anthropic trains employees to recognize specific model failure patterns on an ongoing basis ("ever-boarding"), treating output evaluation as a continuously developed skill rather than a one-time lesson.
Most LLM failures come from poorly written system prompts, not model selection. This guide covers the specific structures (XML tags for Claude, markdown for GPT), token budgeting strategies, and five patterns that consistently work—like making roles absurdly specific, showing exact output schemas instead of describing them, and explicitly telling models when to admit they don't know something. Testing matters too: run your prompts through happy paths, edge cases, and adversarial inputs multiple times before deploying.
- A 400-token system prompt outperformed a bloated 3,000-token version because the model got confused parsing everything at once
- Showing exact JSON schemas instead of describing them jumped format compliance from 70% to 95%+
- Claude performs 15-20% better with XML tags while GPT prefers markdown with JSON schemas and Gemini prefers markdown with numbered rules
- One ambiguous instruction (like "include all relevant context" next to a JSON format requirement) can cause a week of debugging
The author argues that treating prompts as disposable is wasteful—instead, build modular "skills" (organized folders with instructions and scripts) that you improve over time and chain together. He breaks down 17 tactics from Anthropic engineers into 6 layers, showing how to structure workflows so improvements compound across tasks rather than starting from scratch each time.
- The author rebuilt a 600-line SEO prompt into six modular skill files (research, audit, metadata, internal links, GSC monitoring, title scoring), and improving one now improves every workflow that uses it.
- The core shift is from one-off prompting to building reusable "skills"—folders with a SKILL.md file, scripts, and references—that Claude runs identically every time and that get refined after each failure.
- Anthropic engineers' 17 tactics get grouped into 6 layers, with the top priority being: stop writing custom prompts for repetitive tasks and instead build slash-command-invoked skills.
- The lasting value comes from the infrastructure built on top of Claude, not from any single prompt, which evaporates once the session ends.
PixMind’s Nano Banana Pro uses Gemini 3 Pro to generate posters, labels, infographics, and ads with sharp text rendering, multilingual localization, and high-res output. It handles complex prompts with multiple objects, layout constraints, and cultural context, and offers a simple review-and-export workflow with adjustable resolutions and aspect ratios for professional projects.
- Nano Banana Pro (built on Gemini 3 Pro) outputs at 1K/2K/4K with selectable aspect ratios (1:1, 4:3, 3:4, 16:9, 9:16)
- Renders crisp, readable text directly in images (menus, labels, charts) without post-editing
- Maintains consistent colors, fonts, and imagery across localized/multilingual versions of the same design
- Handles complex multi-object, multi-step prompts (process diagrams, comparison layouts) via a simple prompt-review-export workflow
A team member at Anthropic shared the exact LOOPS.md file Andrej Karpathy uses. When loaded into Claude, it shifted the model from generic replies to responses tailored to the user’s thinking. The approach highlights building a system prompt layer rather than chatting directly with the model.
- A supposed Anthropic teammate shared Karpathy's "LOOPS.md" file, claimed to be his personal prompt-engineering setup for Claude.
- The pitch is that using it shifts Claude from generic answers to step-by-step responses matching the user's own thinking style.
- The core concept: treat Claude as wrapped in a system layer (templates, token budgets, chain-of-thought triggers, multi-pass refinement protocols) rather than prompting it directly.
- Framed as urgent/scarce content ("save it before it disappears"), a hallmark of unverifiable social-media hype rather than a sourced claim.
This post shares a two-week-tested prefix prompt designed to make an AI respond as a world-class expert. It instructs the AI to verify every fact, use step-by-step reasoning, avoid disclaimers or moralizing, and deliver detailed, assertive answers with explicit confidence levels.
- A viral prefix prompt (attributed to @pmarca) reframes AI as an aggressively honest expert that fact-checks itself, shows step-by-step reasoning, and admits when it doesn't know something.
- It explicitly bans hedging, disclaimers, and flattery like "great question," forcing the AI to lead with counterarguments to flawed premises instead.
- It requires the AI to tag conclusions with explicit confidence levels (high/moderate/low/unknown) and to hold its position unless given new evidence.
An Anthropic team member shared the internal Claude.md prompt template that Andrej Karpathy uses. Applying this file made Claude stop resisting and deliver exactly the responses the author needed.
- A tweet claims an Anthropic team member shared an internal "Claude.md" prompt template attributed to Andrej Karpathy that reportedly makes Claude follow instructions more reliably.
- Karpathy reportedly joined Anthropic five weeks before this post.
- The file allegedly includes formatting rules, tone settings, and error-handling steps meant to reduce vague or evasive Claude responses.
- The claims come from secondhand reports (a "friend") rather than verified sourcing or a linked deep-dive.
The article argues that when you find yourself repeating the same prompts with AI agents, it’s smarter to build a “loop” – a system combining intent, context, action, evaluation, memory, and a stop condition – rather than manually steering each step. It shows how to calculate when a loop’s upfront cost pays off over repeated tasks and gives examples ranging from CI checks to goal-based agent scripts.
- Break-even formula for automating a task: P × N × (S + R) > F, where reliability, run count, and time/risk saved must outweigh setup cost.
- A 90-minute "shipping skill" script pays off after ~8 PRs if it saves 10 min per PR plus 5 min of review drag.
- A 4-hour repo-triage script saving 35 min/day breaks even in ~10 workdays at ~70% reliability.
- The real bottleneck shifts from doing the work by hand to noticing which recurring tasks are worth turning into a self-sustaining loop.
Hand-tuned natural language prompts make AI prototypes easy but lead to “prompt debt”—bloated, brittle instructions that slow development, confuse teams, and lock you into a single model. The author argues for replacing manual prompt fixes with measurable specs, automated prompt search, and testing to keep systems reliable and portable.
- Hand-tuned prompts accumulate into brittle "prompt debt" as fixes pile up for every new edge case, and that fragility locks teams into whichever model the prompt was tuned for (GPT-4o1 still dominated traffic this spring despite newer releases).
- Small, irrelevant framing changes wildly swing model behavior: patient vs. physician phrasing flipped refusal rates from zero to ten in a clinical study, and just mentioning a favorite NFL team changed how often models dodged sensitive topics in Harvard research.
- Prompt authors compensate for this unpredictability by repeating rules obsessively—Claude's coding prompt issues seven tool-call warnings, Fable's leaked instructions repeat a copyright rule six times—which only adds fragility.
- The fix is treating prompts like code: define pass/fail test cases and metrics, then use automated search tools (DSPy, GEPA) to optimize prompts, making model swaps take hours instead of weeks.
A former Meta L8 engineer shares his end-to-end agentic workflow, from terminal setup to prompt strategies. He outlines the tools (WezTerm, tmux, Neovim, Claude Code/OpenCode), voice input, and delegation techniques that let him manage AI agents like a dev team.
- A former Meta L8 produces 30+ high-quality PRs in a single day by managing AI agents instead of writing code himself
- His entire workflow runs in a terminal (WezTerm + tmux + Neovim) using Claude Code for Anthropic models and OpenCode for everything else, avoiding vendor lock-in features like auto-memory
- He pairs local, offline speech recognition with terminal prompts to iterate faster, treating each prompt like a spec refined against a "golden set" of expected outputs
- He's built custom CLI tools to automate context loading, versioning, and error handling so prompts plug into a repeatable pipeline
This article shows how to replicate Stanford’s STORM research workflow inside Claude with four simple prompts. You generate five expert perspectives, map their contradictions, synthesize findings, and peer-review the results—all in five minutes instead of 40+ hours.
- Four sequential Claude prompts (five expert perspectives → conflict mapping → synthesis → self-critique) replicate Stanford's STORM research method in about five minutes instead of 40-60 hours of grad-student research.
- Stanford's blind tests found STORM-style output 25% more organized and 10% broader than standard single-prompt responses.
- The method requires no code or signup and works for tasks like article prep, business briefings, negotiation planning, or job interview prep.
- The advantage is temporary—expect mainstream tools to bake in multi-perspective workflows within about 18 months.
Ponytail is a plugin and ruleset for AI coding agents that enforces a six-step minimal-code ladder—skip unnecessary code, prefer stdlib or native features, then one-liners—to produce only what each task needs. Benchmarks on Claude models show 80–94% less code, 3–6× faster runs, and 42–75% lower cost. Installation covers Claude Code, Codex, OpenCode, Gemini/Antigravity CLI, Copilot, ClawHub, and more.
- Ponytail enforces a six-step "minimal code" decision ladder before an AI agent writes anything, starting with "does this need to exist" and falling back to stdlib/native features before custom code.
- Benchmarks on five tasks (run 30x for cost, 10x for code/latency) show 80–94% less code, 3–6x faster responses, and 42–75% lower cost versus a vanilla agent.
- It tags every shortcut with a "ponytail" comment for traceability, and preserves validation, error handling, security, and accessibility rather than golfing code.
- Installation is two small Node.js hooks across Claude Code, Codex, Copilot, Gemini/Antigravity, and others, adding slash commands (/ponytail lite|full|ultra|off, /ponytail-review, /ponytail-audit, /ponytail-debt) to control and audit the shortcuts.
This article traces the evolution of AI loops—small programs that run, check, and re-prompt coding agents—from early ReAct and AutoGPT examples to today’s durable, multi-agent orchestration with scheduling and self-verification. It shows why loop management, not model calls, is now the biggest cost in AI coding and outlines best practices: cap iterations, build reusable skills, and include feedback checkpoints.
- Boris Cherny landed 259 PRs in 30 days by having loops handle all the prompting instead of him
- Loops have evolved through five stages, from ReAct/AutoGPT to today's self-scheduling, crash-recoverable multi-agent systems
- The real difference from plain cronjobs is a built-in decision engine: loops read state, decide next steps, and validate results rather than just firing a fixed script
- Cherny's practical advice centers on self-verification, auto-permissions, and using /loop or /goal commands so agents catch their own mistakes
The article shows that when an LLM evaluates if text meets a given criterion, the answer already sits in its hidden state before any token is generated. By capturing the hidden representation at a designated seed token and training a small MLP head (with optional LoRA sharpening and isotonic calibration), you get a fast, calibrated classifier that accepts arbitrary English criteria without per-criterion retraining.
- LLMs encode the yes/no answer to a criterion in their hidden states before generating any text, so you can extract a calibrated probability from the hidden state at a seed token (~70% layer depth) instead of generating output.
- A small probe (MLP/linear head) trained on hidden states from a few thousand labeled triples, plus isotonic calibration, generalizes to arbitrary unseen criteria without retraining.
- Training a LoRA to produce one-sentence verdicts (but only reading the resulting hidden state, never generating) sharpens the signal by reshaping the residual stream geometry at the seed position.
- Prefilling content once and treating criteria as short continuations enables cache-efficient scoring against many criteria, though it can fail on cases requiring deep criterion-content interaction, which still need full cross-encoder prompts.
Boris Cherny breaks down nine common habits that burn most of your Claude tokens before the model even sees your prompt—loading CLAUDE.md, rereading chat history, forgotten hooks, and more. He shows how each pattern eats into your limits and why complaints about “Claude getting dumber” usually miss the real culprit.
- The article provides no actual list of the nine habits, specific hooks, or countermeasures beyond vague category names—despite claiming precise percentages for each.
- The claimed source (a tweet/profile labeled "Mnimiy @Mnilax") doesn't match the detailed narrative about Boris Cherny, a podcast episode, and 400 hours of usage data, suggesting fabricated or unverifiable attribution.
- The specific statistics (73% total, 14% for CLAUDE.md, 13% for chat history, 11% for hooks) are presented with false precision but no methodology or source is given for how they were measured.