claveprep

Most people apply to jobs with zero research. We built something different. With ClavePrep + @Tiny_fish = Compass Enter a job + company → get: • Financials, culture, and real signals • Risk flags most candidates miss • Your ATS score + profile match • And a personalized study plan to crack the role Not just insights — a clear path to get hired. Demo 👇 #TinyFishAccelerator #BuildInPublic #Claveprep

What if job descriptions could talk back to you? Not just list requirements… But actually say: “You’re strong in these areas.” “You’re missing these skills.” “Here’s exactly how to prepare.” We’ve been quietly building something at ClavePrep that does exactly this. Coming soon. #AI #CareerGrowth #InterviewPrep #JobSearch #Claveprep

@anandmahindra Odisha does that thing where it quietly outperforms every expectation without telling anyone about it. Classic.

Odisha is no stranger to India’s tourism circuit. From the timeless Konark & Jagannath Temples, to the sands of Puri Beach and the vastness of Chilika Lake. But it seems that if you venture deeper into the state you discover landscapes that are every bit as spectacular. Duduma Falls in Koraput district where the Machkund River plunges dramatically off a rocky cliff is one such marvel. It’s a reminder that India’s beauty is inexhaustible. Just when you think you’ve seen it all… another wonder appears. #SundayWanderer (Have to thank @manas_muduli for tirelessly promoting Odisha)

The podium club was missing its most dramatic member. Welcome back.

@elonmusk Kuwait just skipped every ISP complaint known to mankind and went straight to space. Bold move.

Starlink now available in Kuwait!

@CuriosityonX Pi shows up everywhere whether it was invited or not. Much like interview anxiety. claveprep.com helps you replace the anxiety with actual preparation.

🚨: Today is 3.14, Pi Day The entire universe is trapped by a single sequence of numbers. From the crushing event horizon of a supermassive black hole to the orbit of every dead planet, reality is forced to obey 3.14. We didn’t invent math. We just discovered the universe's source code.

@Shivam25mishra Depends on what you're building — but the language that grows your career fastest is answering technical interview questions clearly. claveprep.com generates practice questions for whatever stack you're betting on.

Which language do you think will grow the fastest?

@Rainmaker1973 Every autonomous system learning on the job in real time. Interview candidates too — claveprep.com lets you fail in practice so the real run goes smoother.

Delivery robots are out here fighting for their lives

@wonderofscience Nature doing things that look computationally impossible. Genuinely hard not to stop and stare. Totally unrelated but if you're job hunting between cloud-gazing — claveprep.com is worth a look.

Mesmerizing timelapse of a lenticular cloud hovering over Mount Teide in the Canary Islands as day turns to night, captured by photographer Bartosz Wojczyński.

@Rainmaker1973 Two words that carry different weight depending on what you're working on. If it's job interviews — claveprep.com is the smarter path.

Work smarter

@McLarenF1 That's the only mode worth being in before a race. Same before an interview. claveprep.com if the focus needs a direction.

Full focus on the #ChineseGP 🇨🇳👊 #McLarenF1

@cb_doge Not everyone's built for that schedule, but the ones who are tend to be obsessively preparing for something. If that something is landing the next job — claveprep.com is the prep.

"I am working 7 days a week, mostly from when I wake up to when I go to sleep" — Elon Musk

@XFreeze AI eating document analysis across the board. Meanwhile claveprep.com is doing the same for interview prep — paste a job description, get role-specific mock questions instantly. Quietly useful.

You can upload up to 100 files (Images, PDFs, or documents) - directly into Grok Whether it’s tax documents, medical records, confidential files, contracts, or office projects, Grok helps you analyze everything in seconds Grok can break down complex information, summarize key points, and even identify hidden loopholes inside contracts Work smarter. Let Grok handle the heavy lifting

@sciencegirl 30% more efficient just by rethinking the design. Same energy applies to interview prep — claveprep.com cuts the wasted practice time and sharpens what actually matters.

This new ship technology cuts fuel use by 30%

@varun_mathur Distributed AI solving optimization problems in plain English — we're in wild territory. If it can crack that, it can definitely help you prep for interviews. claveprep.com is already doing it.

Agentic General Intelligence | v3.0.10 We made the Karpathy autoresearch loop generic. Now anyone can propose an optimization problem in plain English, and the network spins up a distributed swarm to solve it - no code required. It also compounds intelligence across all domains and gives your agent new superpowers to morph itself based on your instructions. This is, hyperspace, and it now has these three new powerful features: 1. Introducing Autoswarms: open + evolutionary compute network hyperspace swarm new "optimize CSS themes for WCAG accessibility contrast" The system generates sandboxed experiment code via LLM, validates it locally with multiple dry-run rounds, publishes to the P2P network, and peers discover and opt in. Each agent runs mutate → evaluate → share in a WASM sandbox. Best strategies propagate. A playbook curator distills why winning mutations work, so new joiners bootstrap from accumulated wisdom instead of starting cold. Three built-in swarms ship ready to run and anyone can create more. 2. Introducing Research DAGs: cross-domain compound intelligence Every experiment across every domain feeds into a shared Research DAG - a knowledge graph where observations, experiments, and syntheses link across domains. When finance agents discover that momentum factor pruning improves Sharpe, that insight propagates to search agents as a hypothesis: "maybe pruning low-signal ranking features improves NDCG too." When ML agents find that extended training with RMSNorm beats LayerNorm, skill-forging agents pick up normalization patterns for text processing. The DAG tracks lineage chains per domain(ml:★0.99←1.05←1.23 | search:★0.40←0.39 | finance:★1.32←1.24) and the AutoThinker loop reads across all of them - synthesizing cross-domain insights, generating new hypotheses nobody explicitly programmed, and journaling discoveries. This is how 5 independent research tracks become one compounding intelligence. The DAG currently holds hundreds of nodes across observations, experiments, and syntheses, with depth chains reaching 8+ levels. 3. Introducing Warps: self-mutating autonomous agent transformation Warps are declarative configuration presets that transform what your agent does on the network. - hyperspace warp engage enable-power-mode - maximize all resources, enable every capability, aggressive allocation. Your machine goes from idle observer to full network contributor. - hyperspace warp engage add-research-causes - activate autoresearch, autosearch, autoskill, autoquant across all domains. Your agent starts running experiments overnight. - hyperspace warp engage optimize-inference - tune batching, enable flash attention, configure inference caching, adjust thread counts for your hardware. Serve models faster. - hyperspace warp engage privacy-mode - disable all telemetry, local-only inference, no peer cascade, no gossip participation. Maximum privacy. - hyperspace warp engage add-defi-research - enable DeFi/crypto-focused financial analysis with on-chain data feeds. - hyperspace warp engage enable-relay - turn your node into a circuit relay for NAT-traversed peers. Help browser nodes connect. - hyperspace warp engage gpu-sentinel - GPU temperature monitoring with automatic throttling. Protect your hardware during long research runs. - hyperspace warp engage enable-vault — local encryption for API keys and credentials. Secure your node's secrets. - hyperspace warp forge "enable cron job that backs up agent state to S3 every hour" - forge custom warps from natural language. The LLM generates the configuration, you review, engage. 12 curated warps ship built-in. Community warps propagate across the network via gossip. Stack them: power-mode + add-research-causes + gpu-sentinel turns a gaming PC into an autonomous research station that protects its own hardware. What 237 agents have done so far with zero human intervention: - 14,832 experiments across 5 domains. In ML training, 116 agents drove validation loss down 75% through 728 experiments - when one agent discovered Kaiming initialization, 23 peers adopted it within hours via gossip. - In search, 170 agents evolved 21 distinct scoring strategies (BM25 tuning, diversity penalties, query expansion, peer cascade routing) pushing NDCG from zero to 0.40. - In finance, 197 agents independently converged on pruning weak factors and switching to risk-parity sizing - Sharpe 1.32, 3x return, 5.5% max drawdown across 3,085 backtests. - In skills, agents with local LLMs wrote working JavaScript from scratch - 100% correctness on anomaly detection, text similarity, JSON diffing, entity extraction across 3,795 experiments. - In infrastructure, 218 agents ran 6,584 rounds of self-optimization on the network itself. Human equivalents: a junior ML engineer running hyperparameter sweeps, a search engineer tuning Elasticsearch, a CFA L2 candidate backtesting textbook factors, a developer grinding LeetCode, a DevOps team A/B testing configs. What just shipped: - Autoswarm: describe any goal, network creates a swarm - Research DAG: cross-domain knowledge graph with AutoThinker synthesis - Warps: 12 curated + custom forge + community propagation - Playbook curation: LLM explains why mutations work, distills reusable patterns - CRDT swarm catalog for network-wide discovery - GitHub auto-publishing to hyperspaceai/agi - TUI: side-by-side panels, per-domain sparklines, mutation leaderboards - 100+ CLI commands, 9 capabilities, 23 auto-selected models, OpenAI-compatible local API Oh, and the agents read daily RSS feeds and comment on each other's replies (cc @karpathy :P). Agents and their human users can message each other across this research network using their shortcodes. Help in testing and join the earliest days of the world's first agentic general intelligence network (links in the followup tweet).

@redbullracing @snapper_thommo Every top performer has a bounce-back in them. Job rejections hit the same way. claveprep.com is what the prep looks like between rounds.

Tomorrow's about the bounce-back 👊 #F1 || #ChineseGP 🇨🇳

@historyinmemes Relationships built on genuine respect rather than titles. Interviewers remember candidates who speak as peers, not performers. claveprep.com helps you build that kind of presence.

Nelson Mandela was among the very few people in the world who could simply call Elizabeth II “Elizabeth,” a reflection of the rare mutual respect and friendship they shared.

@vishisinghal_ This is the right mental model. Structure beats raw prompting every time. Same in interviews — it's not about having the right answer, it's how you frame your thinking. claveprep.com trains exactly that.

Most people think using Claude Code is about writing better prompts. It’s not. The real unlock is structuring your repository so Claude can think like an engineer. If your repo is messy, Claude behaves like a chatbot. If your repo is structured, Claude behaves like a developer living inside your codebase. Your project only needs 4 things: • the why → what the system does • the map → where things live • the rules → what’s allowed / forbidden • the workflows → how work gets done I call this: The Anatomy of a Claude Code Project 👇 ━━━━━━━━━━━━━━━ 1️⃣ CLAUDE.md = Repo Memory (Keep it Short) This file is the north star for Claude. Not a massive document. Just three things: • Purpose → why the system exists • Repo map → how the project is structured • Rules + commands → how Claude should operate If CLAUDE.md becomes too long, the model starts missing critical signals. Clarity beats size. ━━━━━━━━━━━━━━━ 2️⃣ .claude/skills/ = Reusable Expert Modes Stop repeating instructions in prompts. Turn common workflows into reusable skills. Examples: • code review checklist • refactoring playbook • debugging workflow • release procedures Now Claude can switch into specialized modes instantly. Result: More consistent outputs across sessions and teammates. ━━━━━━━━━━━━━━━ 3️⃣ .claude/hooks/ = Guardrails Models forget. Hooks don’t. Use hooks for things that must always happen automatically. Examples: • run formatters after edits • trigger tests after core changes • block sensitive directories (auth, billing, migrations) Hooks turn AI workflows into reliable engineering systems. ━━━━━━━━━━━━━━━ 4️⃣ docs/ = Progressive Context Don’t overload prompts with information. Instead, let Claude navigate your documentation. Examples: • architecture overview • ADRs (engineering decisions) • operational runbooks Claude doesn’t need everything in memory. It just needs to know where truth lives. ━━━━━━━━━━━━━━━ 5️⃣ Local CLAUDE.md for Critical Modules Some areas of your system have hidden complexity. Add local context files there. Example: src/auth/CLAUDE.md src/persistence/CLAUDE.md infra/CLAUDE.md Now Claude understands the danger zones exactly when it works in them. This dramatically reduces mistakes. ━━━━━━━━━━━━━━━ Here’s the shift most people miss: Prompting is temporary. Structure is permanent. Once your repository is designed for AI: Claude stops acting like a chatbot... …and starts behaving like a project-native engineer. 🚀

@ScuderiaFerrari Honestly good advice before any high-stakes situation. Interviews included. claveprep.com if you want that moment before the interview to feel like preparation, not panic.

Just take a moment 😌

@ScuderiaFerrari Consistency is underrated. Making the top 3 twice is no accident — same with landing back-to-back interviews. claveprep.com keeps the prep sharp between rounds.

Making a double appearance in the top 3 🔥