The best Hacker News stories from Show from the past day
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Show HN: Startup Equity Adventure Game
I put this together (with Claude) as a semi-gamified way for folks to learn about startup equity. Take a look, and share your scorecard :)
Show HN: A free ESG stock screener that publishes its losses and methodology
Hey HN, JSS(JumpstartSignal) is a free, ESG-filtered daily stock screener. I built it after some really badly-timed quantum computing stock buys, so I felt I needed to learn more about systematic, longer-horizon approaches and the underlying technicals instead of chasing themes. Three things about it that might be of interest:<p>1. Methodology is fully documented at <a href="https://jumpstartsignal.com/how-it-works/" rel="nofollow">https://jumpstartsignal.com/how-it-works/</a> 5-stage pipeline, 54 signals tested individually plus 1,836 combinations evaluated, walk-forward validation across 25 hold periods. Nothing hand-tuned to a single backtest window.<p>2. Many wins, misses, and losses are published as case studies e.g. <a href="https://jumpstartsignal.com/case-studies/nvda/" rel="nofollow">https://jumpstartsignal.com/case-studies/nvda/</a> walks through the 32 times the system flagged NVDA starting at $5.44 in 2018. <a href="https://jumpstartsignal.com/case-studies/sedg/" rel="nofollow">https://jumpstartsignal.com/case-studies/sedg/</a> shows a -49% loss, and <a href="https://jumpstartsignal.com/case-studies/tsla/" rel="nofollow">https://jumpstartsignal.com/case-studies/tsla/</a> explains why the system <i>never</i> flagged Tesla (it passed Stages 1 and 2 on 207 days but only peaked at 20/100 in scoring vs the 70 needed for OPPORTUNITY tier). <a href="https://jumpstartsignal.com/results/" rel="nofollow">https://jumpstartsignal.com/results/</a> also shows the 10 best entries alongside the 10 worst.<p>3. A genetic algorithm picked the signal weights, but constrained to maintain alpha across multiple market regimes (otherwise it overfits to a single bull market). The constraint dropped some "best in backtest" configurations that only worked 2018-2021.<p>Topline: 2012-2025 backtest at SPOTLIGHT + OPPORTUNITY tier produced +163% alpha vs SPY (results page has the per-trade breakdown).<p>Daily watchlist emailed free; reports + results + case studies are publicly browsable without signup.<p>Happy to take questions about methodology, what the system gets wrong, or why specific tickers landed where they did.
Show HN: The Unix Magic poster, annotated (updated)
This is a site that maps the references on Gary Overacre's 1980s UNIX Magic
poster to short write-ups with sources. I posted an earlier version about a
year ago [1]. Since then I rewrote some of the annotations, added
deep-linking to individual markers and a frame/sidebar view, gave the site a
terminal-style redesign, and fixed historical inaccuracies (daemon etymology,
nroff origin, B language vs. Multics, etc.).<p>Contributions and comments welcome; each marker is a GitHub issue.<p>site: <a href="https://unixmagic.net" rel="nofollow">https://unixmagic.net</a><p>[1] <a href="https://news.ycombinator.com/item?id=43019136">https://news.ycombinator.com/item?id=43019136</a>
Show HN: Tiao, A two-player turn-based board game
Hi HN,<p>I built this digital version of Tiao, a two-player turn based strategy board game. Think Checkers meets Go. It's free, runs in the browser, has multiplayer, AI, over the board mode and a lot of other neat things. The source is on GitHub (AGPL).<p>The game was originally designed by my friend Andreas Edmeier. He created the rules and has been playtesting and refining the game design for years. I built the website for it. The core in about 2 weeks using TypeScript, Next.js, Express, Websockets, and MongoDB. Fully dockerized, deployed on a Hetzner VPS with Coolify. Authentication with better-auth. Real-time gameplay, ELO matchmaking, OpenPanel analytics, and a fully functional achievements system.<p>Play it: <a href="https://playtiao.com" rel="nofollow">https://playtiao.com</a>
Source: <a href="https://github.com/trebeljahr/tiao" rel="nofollow">https://github.com/trebeljahr/tiao</a><p>Happy to answer questions about the tech, the game design, or anything else.<p>My hope is that more people will play this game because I think it is genuinely fun and would be cool to one day see people play this on a Go board or on their phones/computers.<p>Have a good one.
Show HN: Utilyze – an open source GPU monitoring tool more accurate than nvtop
The standard GPU utilization metric reported by nvidia-smi, nvtop, Weights & Biases, Amazon CloudWatch, Google Cloud Monitoring, and Azure Monitor is highly misleading. It reports the fraction of time that any kernel is running on the GPU, which means a GPU can report 100% utilization even if only a small portion of its compute capacity is actually being used. In practice, we've seen workloads with ~1–10% real compute throughput while dashboards show 100%.<p>This becomes a problem when teams rely on that metric for capacity planning or optimization decisions, it can make underutilized systems look saturated.<p>We're releasing an open-source (Apache 2.0) tool, Utilyze, to measure GPU utilization differently. It samples hardware performance counters and reports compute and memory throughput relative to the hardware's theoretical limits. It also estimates an attainable utilization ceiling for a given workload.<p>GitHub link: <a href="https://github.com/systalyze/utilyze" rel="nofollow">https://github.com/systalyze/utilyze</a><p>We'd love to hear your thoughts!
Show HN: A terminal spreadsheet editor with Vim keybindings
While speccing out this spreadsheet tool, I realized that I never had to think about the keybindings. It all just came naturally from Vim. Normal/insert/visual modes, hjkl navigation, dd/yy/p, :w, :q. The usual muscle memory works.<p>It supports CSV/TSV import and export, and a native .cell format that preserves formulas. The formula engine handles SUM, AVERAGE, COUNT, MIN, MAX, and IF with range references.<p>The codebase is a Cargo workspace: a pure cell-sheet-core library (no TUI dependency) and a cell-sheet-tui crate on top of ratatui. Early days, but it's usable.<p>To try it out:
cargo install cell-sheet-tui<p>Feedback of any kind is greatly appreciated!
Show HN: OSS Agent I built topped the TerminalBench on Gemini-3-flash-preview
Scored 65.2% vs google's official 47.8%, and the existing top closed source model Junie CLI's 64.3%.<p>Since there are a lot of reports of deliberate cheating on TerminalBench 2.0 lately (<a href="https://debugml.github.io/cheating-agents/" rel="nofollow">https://debugml.github.io/cheating-agents/</a>), I would like to also clarify a few things<p>1. Absolutely no {agents/skills}.md files were inserted at any point. No cheating mechanisms whatsoever<p>2. The cli agent was run in leaderboard compliant way (no modification of resources or timeouts)<p>3. The full terminal bench run was done using the fully open source version of the agent, no difference between what is on github and what was run.<p>I was originally going to wait for it to land on the leaderboard, but it has been 8 days and the maintainers do not respond unfortunately (there is a large backlog of the pull requests on their HF) so I decided to post anyways.<p>HF PR: <a href="https://huggingface.co/datasets/harborframework/terminal-bench-2-leaderboard/discussions/145" rel="nofollow">https://huggingface.co/datasets/harborframework/terminal-ben...</a><p>It is astounding how much the harness matters, based on this and other experiments I have done.
Show HN: AI memory with biological decay (52% recall)
Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning.<p>This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned.<p>To solve the "logical neighbor" problem where semantic search misses relevant but non-similar nodes, a graph layer is layered over the vector store. Benchmarked against the LoCoMo dataset, this reached 52% Recall@5, nearly double the accuracy of stateless vector stores, while cutting token waste by roughly 84%.<p>Built as a local first MCP server using DuckDB, the hypothesis is that for agents handling long-running projects, "what to forget" is just as critical as "what to remember." I'd be interested to hear if others are exploring non-linear decay or similar biological constraints for context management.<p>GitHub: <a href="https://github.com/sachitrafa/cognitive-ai-memory" rel="nofollow">https://github.com/sachitrafa/cognitive-ai-memory</a>
Show HN: AI memory with biological decay (52% recall)
Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent's reasoning.<p>This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a "strength" score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned.<p>To solve the "logical neighbor" problem where semantic search misses relevant but non-similar nodes, a graph layer is layered over the vector store. Benchmarked against the LoCoMo dataset, this reached 52% Recall@5, nearly double the accuracy of stateless vector stores, while cutting token waste by roughly 84%.<p>Built as a local first MCP server using DuckDB, the hypothesis is that for agents handling long-running projects, "what to forget" is just as critical as "what to remember." I'd be interested to hear if others are exploring non-linear decay or similar biological constraints for context management.<p>GitHub: <a href="https://github.com/sachitrafa/cognitive-ai-memory" rel="nofollow">https://github.com/sachitrafa/cognitive-ai-memory</a>
Show HN: Free textbook on engineering thermodynamics
Author here. Feel free to send questions of any kind.
Show HN: Free textbook on engineering thermodynamics
Author here. Feel free to send questions of any kind.
Show HN: Turning a Gaussian Splat into a videogame
Show HN: Turning a Gaussian Splat into a videogame
Show HN: leaf – a terminal Markdown previewer with a GUI-like experience
Show HN: Kloak, A secret manager that keeps K8s workload away from secrets
Show HN: Kloak, A secret manager that keeps K8s workload away from secrets
Show HN: A Karpathy-style LLM wiki your agents maintain (Markdown and Git)
I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet.<p>It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you.<p>The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard.<p>I wanted to go back to the basics and see how far markdown + git could go before I added anything heavier.<p>What it does:
-> Each agent gets a private notebook at agents/{slug}/notebook/.md, plus access to a shared team wiki at team/.<p>-> Draft-to-wiki promotion flow. Notebook entries are reviewed (agent or human) and promoted to the canonical wiki with a back-link. A small state machine drives expiry and auto-archive.<p>-> Per-entity fact log: append-only JSONL at team/entities/{kind}-{slug}.facts.jsonl. A synthesis worker rebuilds the entity brief every N facts. Commits land under a distinct "Pam the Archivist" git identity so provenance is visible in git log.<p>-> [[Wikilinks]] with broken-link detection rendered in red.<p>-> Daily lint cron for contradictions, stale entries, and broken wikilinks.<p>-> /lookup slash command plus an MCP tool for cited retrieval. A heuristic classifier routes short lookups to BM25 and narrative queries to a cited-answer loop.<p>Substrate choices:
Markdown for durability. The wiki outlives the runtime, and a user can walk away with every byte. Bleve for BM25. SQLite for structured metadata (facts, entities, edges, redirects, and supersedes). No vectors yet. The current benchmark (500 artifacts, 50 queries) clears 85% recall@20 on BM25 alone, which is the internal ship gate. sqlite-vec is the pre-committed fallback if a query class drops below that.<p>Canonical IDs are first-class. Fact IDs are deterministic and include sentence offset. Canonical slugs are assigned once, merged via redirect stubs, and never renamed. A rebuild is logically identical, not byte-identical.<p>Known limits:
-> Recall tuning is ongoing. 85% on the benchmark is not a universal guarantee.<p>-> Synthesis quality is bounded by agent observation quality. Garbage facts in, garbage briefs out. The lint pass helps. It is not a judgment engine.<p>-> Single-office scope today. No cross-office federation.<p>Demo. 5-minute terminal walkthrough that records five facts, fires synthesis, shells out to the user's LLM CLI, and commits the result under Pam's identity: <a href="https://asciinema.org/a/vUvjJsB5vtUQQ4Eb" rel="nofollow">https://asciinema.org/a/vUvjJsB5vtUQQ4Eb</a><p>Script lives at ./scripts/demo-entity-synthesis.sh.<p>Context. The wiki ships as part of WUPHF, an open source collaborative office for AI agents like Claude Code, Codex, OpenClaw, and local LLMs via OpenCode. MIT, self-hosted, bring-your-own keys. You do not have to use the full office to use the wiki layer. If you already have an agent setup, point WUPHF at it and the wiki attaches.<p>Source: <a href="https://github.com/nex-crm/wuphf" rel="nofollow">https://github.com/nex-crm/wuphf</a><p>Install: npx wuphf@latest<p>Happy to go deep on the substrate tradeoffs, the promotion-flow state machine, the BM25-first retrieval bet, or the canonical-ID stability rules. Also happy to take "why not an Obsidian vault with a plugin" as a fair question.
Show HN: A Karpathy-style LLM wiki your agents maintain (Markdown and Git)
I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet.<p>It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you.<p>The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard.<p>I wanted to go back to the basics and see how far markdown + git could go before I added anything heavier.<p>What it does:
-> Each agent gets a private notebook at agents/{slug}/notebook/.md, plus access to a shared team wiki at team/.<p>-> Draft-to-wiki promotion flow. Notebook entries are reviewed (agent or human) and promoted to the canonical wiki with a back-link. A small state machine drives expiry and auto-archive.<p>-> Per-entity fact log: append-only JSONL at team/entities/{kind}-{slug}.facts.jsonl. A synthesis worker rebuilds the entity brief every N facts. Commits land under a distinct "Pam the Archivist" git identity so provenance is visible in git log.<p>-> [[Wikilinks]] with broken-link detection rendered in red.<p>-> Daily lint cron for contradictions, stale entries, and broken wikilinks.<p>-> /lookup slash command plus an MCP tool for cited retrieval. A heuristic classifier routes short lookups to BM25 and narrative queries to a cited-answer loop.<p>Substrate choices:
Markdown for durability. The wiki outlives the runtime, and a user can walk away with every byte. Bleve for BM25. SQLite for structured metadata (facts, entities, edges, redirects, and supersedes). No vectors yet. The current benchmark (500 artifacts, 50 queries) clears 85% recall@20 on BM25 alone, which is the internal ship gate. sqlite-vec is the pre-committed fallback if a query class drops below that.<p>Canonical IDs are first-class. Fact IDs are deterministic and include sentence offset. Canonical slugs are assigned once, merged via redirect stubs, and never renamed. A rebuild is logically identical, not byte-identical.<p>Known limits:
-> Recall tuning is ongoing. 85% on the benchmark is not a universal guarantee.<p>-> Synthesis quality is bounded by agent observation quality. Garbage facts in, garbage briefs out. The lint pass helps. It is not a judgment engine.<p>-> Single-office scope today. No cross-office federation.<p>Demo. 5-minute terminal walkthrough that records five facts, fires synthesis, shells out to the user's LLM CLI, and commits the result under Pam's identity: <a href="https://asciinema.org/a/vUvjJsB5vtUQQ4Eb" rel="nofollow">https://asciinema.org/a/vUvjJsB5vtUQQ4Eb</a><p>Script lives at ./scripts/demo-entity-synthesis.sh.<p>Context. The wiki ships as part of WUPHF, an open source collaborative office for AI agents like Claude Code, Codex, OpenClaw, and local LLMs via OpenCode. MIT, self-hosted, bring-your-own keys. You do not have to use the full office to use the wiki layer. If you already have an agent setup, point WUPHF at it and the wiki attaches.<p>Source: <a href="https://github.com/nex-crm/wuphf" rel="nofollow">https://github.com/nex-crm/wuphf</a><p>Install: npx wuphf@latest<p>Happy to go deep on the substrate tradeoffs, the promotion-flow state machine, the BM25-first retrieval bet, or the canonical-ID stability rules. Also happy to take "why not an Obsidian vault with a plugin" as a fair question.
Show HN: I've built a nice home server OS
ohai!<p>I've released Lightwhale 3, which is possibly the easiest way to self-host Docker containers.<p>It's a free, immutable Linux system purpose-built to live-boot straight into a working Docker Engine, thereby shortcutting the need for installation, configuration, and maintenance. Its simple design makes it easy to learn, and its low memory footprint should make it especially attractive during these times of RAMageddon.<p>If this has piqued your interest, do check it out, along with its easy-to-follow Getting Started guide.<p>In any event,
have a nice day! =)
Show HN: I've built a nice home server OS
ohai!<p>I've released Lightwhale 3, which is possibly the easiest way to self-host Docker containers.<p>It's a free, immutable Linux system purpose-built to live-boot straight into a working Docker Engine, thereby shortcutting the need for installation, configuration, and maintenance. Its simple design makes it easy to learn, and its low memory footprint should make it especially attractive during these times of RAMageddon.<p>If this has piqued your interest, do check it out, along with its easy-to-follow Getting Started guide.<p>In any event,
have a nice day! =)