The best Hacker News stories from Show from the past day

from

All Show

from the past*

day week month

with these search terms:

Create follow Go back

Latest posts:

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: 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: 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: 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: 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: 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! =)