Show HN: Context Mode – 315 KB of MCP output becomes 5.4 KB in Claude Code https://bit.ly/4sePGF4

Show HN: Context Mode – 315 KB of MCP output becomes 5.4 KB in Claude Code Every MCP tool call dumps raw data into Claude Code's 200K context window. A Playwright snapshot costs 56 KB, 20 GitHub issues cost 59 KB. After 30 minutes, 40% of your context is gone. I built an MCP server that sits between Claude Code and these outputs. It processes them in sandboxes and only returns summaries. 315 KB becomes 5.4 KB. It supports 10 language runtimes, SQLite FTS5 with BM25 ranking for search, and batch execution. Session time before slowdown goes from ~30 min to ~3 hours. MIT licensed, single command install: /plugin marketplace add mksglu/claude-context-mode /plugin install context-mode@claude-context-mode Benchmarks and source: https://bit.ly/3MZWN56 Would love feedback from anyone hitting context limits in Claude Code. https://bit.ly/3MZWN56 February 25, 2026 at 07:23AM

Show HN: A Visual Editor for Karabiner https://bit.ly/4sambEf

Show HN: A Visual Editor for Karabiner https://bit.ly/4s30h5E February 25, 2026 at 04:39AM

Show HN: StreamHouse – S3-native Kafka alternative written in Rust https://bit.ly/4kRblAq

Show HN: StreamHouse – S3-native Kafka alternative written in Rust Hey HN, I built StreamHouse, an open-source streaming platform that replaces Kafka's broker-managed storage with direct S3 writes. The goal: same semantics, fraction of the cost. How it works: Producers batch and compress records, a stateless server manages partition routing and metadata (SQLite for dev, PostgreSQL for prod), and segments land directly in S3. Consumers read from S3 with a local segment cache. No broker disks to manage, no replication factor to tune — S3 gives you 11 nines of durability out of the box. What's there today: - Producer API with batching, LZ4 compression, and offset tracking (62K records/sec) - Consumer API with consumer groups, auto-commit, and multi-partition fanout (30K+ records/sec) - Kafka-compatible protocol (works with existing Kafka clients) - REST API, gRPC API, CLI, and a web UI - Docker Compose setup for trying it locally in 5 minutes The cost model is what motivated this. Kafka's storage costs scale with replication factor × retention × volume. With S3 at $0.023/GB/month, storing a TB of events costs ~$23/month instead of hundreds on broker EBS volumes. Written in Rust, ~50K lines across 15 crates. Apache 2.0 licensed. GitHub: https://bit.ly/4tVpwsp Happy to answer questions about the architecture, tradeoffs, or what I learned building this. https://bit.ly/4tVpwsp February 25, 2026 at 03:50AM

Show HN: Enseal – Stop pasting secrets into Slack .env sharing from the terminal https://bit.ly/3MuiGcG

Show HN: Enseal – Stop pasting secrets into Slack .env sharing from the terminal We've all done it — "hey can you DM me the staging .env?" Secrets end up in Slack history, email threads, shared notes — all searchable, all persistent. The secure path (1Password, GPG, etc.) always had more friction than the insecure one, so people took the shortcut. enseal makes the secure path faster than the insecure one: # sender $ enseal share .env Share code: 7-guitarist-revenge Expires: 5 minutes or first receive # recipient $ enseal receive 7-guitarist-revenge ok: 14 secrets written to .env Zero setup, no accounts, no keys needed for basic use. Channels are single-use and time-limited. The relay never sees plaintext (age encryption + SPAKE2 key exchange). For teams that want more: identity mode with public key encryption, process injection (secrets never touch disk), schema validation, at-rest encryption for git, and a self-hostable relay. Written in Rust. MIT licensed. Available via cargo install, prebuilt binaries, or Docker. Looking for feedback on the UX and security model especially. What would make you actually reach for this instead of the Slack DM? Detailed documentation here: https://bit.ly/4ayuSlR https://bit.ly/4qS4t7m February 24, 2026 at 03:15AM

Show HN: Steerling-8B, a language model that can explain any token it generates https://bit.ly/46iquF6

Show HN: Steerling-8B, a language model that can explain any token it generates https://bit.ly/46oj3fy February 24, 2026 at 01:38AM

Show HN: Rendering 18,000 videos in real-time with Python https://bit.ly/3OUFrHg

Show HN: Rendering 18,000 videos in real-time with Python https://bit.ly/4qR82e2 February 22, 2026 at 04:46PM

Show HN: Dq – pipe-based CLI for querying CSV, JSON, Avro, and Parquet files https://bit.ly/3OBlHID

Show HN: Dq – pipe-based CLI for querying CSV, JSON, Avro, and Parquet files I'm a data engineer and exploring a data file from the terminal has always felt more painful than it should be for me. My usual flow involved some combination of avro-tools, opening the file in Excel or sheets, writing a quick Python script, using DataFusion CLI, or loading it into a database just to run one query. It works, but it's friction -- and it adds up when you're just trying to understand what's in a file or track down a bug in a pipeline. A while ago I had this idea of a simple pipe-based CLI tool, like jq but for tabular data, that works across all these formats with a consistent syntax. I refined the idea over time into something I wanted to be genuinely simple and useful -- not a full query engine, just a sharp tool for exploration and debugging. I never got around to building it though. Last week, with AI tools actually being capable now, I finally did :) I deliberately avoided SQL. For quick terminal work, the pipe-based composable style feels much more natural: you build up the query step by step, left to right, and each piece is obvious in isolation. SQL asks you to hold the whole structure in your head before you start typing. `dq 'sales.parquet | filter { amount > 1000 } | group category | reduce total = sum(amount), n = count() | remove grouped | sortd total | head 10'` How it works technically: dq has a hand-written lexer and recursive descent parser that turns the query string into an AST, which is then evaluated against the file lazily where possible. Each operator (filter, select, group, reduce, etc.) is a pure transformation -- it takes a table in and returns a table out. This is what makes the pipe model work cleanly: operators are fully orthogonal and composable in any order. It's written in Go -- single self-contained binary, 11MB, no runtime dependencies, installable via Homebrew. I'd love feedback specially from anyone who's felt the same friction. https://bit.ly/409lnnf February 21, 2026 at 11:31PM

Show HN: Ktop – a themed terminal monitor for GPU, CPU, RAM, temps and OOM kills https://bit.ly/4cI6Wh7

Show HN: Ktop – a themed terminal monitor for GPU, CPU, RAM, temps and OOM kills I built a terminal system monitor that fills a gap I kept hitting when running local LLMs: GPU usage and memory (for both NVidia and AMD) alongside CPU usage and memory, temps, upload, download and OOM kill tracking. All in one view with 50 colour themes. Consumes less cpu usage than glances (in my testing). One line install. https://bit.ly/4qSg1HU February 22, 2026 at 12:45AM

Show HN: AI writes code – humans fix it https://bit.ly/40nlE5P

Show HN: AI writes code – humans fix it https://bit.ly/3ZPmvvX February 21, 2026 at 11:57PM

Show HN: oForum | Self-hostable links/news site https://bit.ly/4c6xzME

Show HN: oForum | Self-hostable links/news site https://bit.ly/3MHNc2K February 20, 2026 at 11:19PM

Show HN: How Amazon Pricing Algorithms Work https://bit.ly/4rrSZZb

Show HN: How Amazon Pricing Algorithms Work Amazon is one of the largest online retailers in the world, offering millions of products across countless categories. Because of this, prices on Amazon can change frequently, which sometimes makes it hard to know if a deal is genuine. Understanding how Amazon pricing works can help shoppers make smarter buying decisions. https://bit.ly/4rqhCpd February 20, 2026 at 11:59PM

Show HN: 17MB model beats human experts at pronunciation scoring https://bit.ly/4cApfor

Show HN: 17MB model beats human experts at pronunciation scoring https://bit.ly/4tLVsPU February 20, 2026 at 04:41AM

Show HN: I indexed the academic papers buried in the DOJ Epstein Files https://bit.ly/46g3g2g

Show HN: I indexed the academic papers buried in the DOJ Epstein Files The DOJ released ~3.5M pages of Epstein documents across 12 datasets. Buried in them are 207 academic papers and 14 books that nobody was really talking about. From what I understand these papers aren't usually freely accesible, but since they are public documents, now they are. I don't know, thought it was interesting to see what this dude was reading. You can check it out at jeescholar.com Pipeline: 1. Downloaded all 12 DOJ datasets + House Oversight Committee release 2. Heuristic pre-filter (abstract detection, DOI regex, citation block patterns, affiliation strings) to cut noise 3. LLM classifier to confirm and extract metadata 4. CrossRef and Semantic Scholar APIs for DOI matching, citation counts, abstracts 5. 87 of 207 papers got DOI matches; the rest are identified but not in major indexes Stack: FastAPI + SQLite (FTS5 for full-text search) + Cloudflare R2 for PDFs + nginx/Docker on Hetzner. The fields represented are genuinely iteresting: there's a cluster of child abuse/grooming research, but also quantum gravity, AGI safety, econophysics, and regenerative medicine. Each paper links back to its original government PDF and Bates number. For sure not an exhaustive list. Would be happy to add more if anyone finds them. https://bit.ly/46g3giM February 20, 2026 at 04:07AM

Show HN: A small, simple music theory library in C99 https://bit.ly/4c569XA

Show HN: A small, simple music theory library in C99 https://bit.ly/4bYYKZH February 19, 2026 at 11:54PM

Show HN: Potatometer – Check how visible your website is to AI search (GEO) https://bit.ly/4tGBki4

Show HN: Potatometer – Check how visible your website is to AI search (GEO) Most SEO tools only check for Google. But a growing chunk of search is now happening inside ChatGPT, Perplexity, and other AI engines, and the signals they use to surface content are different. Potatometer runs multiple checks across both traditional SEO and GEO (Generative Engine Optimization) factors and gives you a score with specific recommendations. Free, no login needed. Curious if others have been thinking about this problem and what signals you think matter most for AI visibility. https://bit.ly/3MtSoXX February 19, 2026 at 07:41AM

Show HN: I built a fuse box for microservices https://bit.ly/3MMHFrH

Show HN: I built a fuse box for microservices Hey HN! I'm Rodrigo, I run distributed systems across a few countries. I built Openfuse because of something that kept bugging me about how we all do circuit breakers. If you're running 20 instances of a service and Stripe starts returning 500s, each instance discovers that independently. Instance 1 trips its breaker after 5 failures. Instance 14 just got recycled and hasn't seen any yet. Instance 7 is in half-open, probing a service you already know is dead. For some window of time, part of your fleet is protecting itself and part of it is still hammering a dead dependency and timing out, and all you can do is watch. Libraries can't fix this. Opossum, Resilience4j, Polly are great at the pattern, but they make per-instance decisions with per-instance state. Your circuit breakers don't talk to each other. Openfuse is a centralized control plane. It aggregates failure metrics from every instance in your fleet and makes the trip decision based on the full picture. When the breaker opens, every instance knows at the same time. It's a few lines of code: const result = await openfuse.breaker('stripe').protect( () => chargeCustomer(payload) ); The SDK is open source, anyone can see exactly what runs inside their services. The other thing I couldn't let go of: when you get paged at 3am, you shouldn't have to find logs across 15 services to figure out what's broken. Openfuse gives you one dashboard showing every breaker state across your fleet: what's healthy, what's degraded, what tripped and when. And, you shouldn't need a deploy to act. You can open a breaker from the dashboard and every instance stops calling that dependency immediately. Planned maintenance window at 3am? Open beforehand. Fix confirmed? Close it instantly. Thresholds need adjusting? Change them in the dashboard, takes effect across your fleet in seconds. No PRs, no CI, no config files. It has a decent free tier for trying it out, then $99/mo for most teams, $399/mo with higher throughput and some enterprise features. Solo founder, early stage, being upfront. Would love to hear from people who've fought cascading failures in production. What am I missing? https://bit.ly/4cHN3qq February 18, 2026 at 03:04PM

Show HN: Codereport – track TODOs, refactors, and bugs in your repo with a CLI https://bit.ly/4kKGzt8

Show HN: Codereport – track TODOs, refactors, and bugs in your repo with a CLI I got tired of TODOs, temporary hacks, and refactors that never get addressed. In most repos I work on: - TODOs are scattered across files/apps/messages - “Critical” fixes don’t actually block people from collecting debt - PR comments or tickets aren’t enough actionable So I built codereport, a CLI that stores structured follow-ups in the repo itself (.codereports/). Each report tracks: - file + line range (src/foo.rs:42-88) - tag (todo, refactor, buggy, critical) - severity (you can configure it to be blocking in CI) - optional expiration date - owner (CODEOWNERS → git blame fallback) You can list, resolve, or delete reports, generate a minimal HTML dashboard with heatmaps and KPIs, and run codereport check in CI to fail merges if anything blocking or expired is still open. It’s repo-first, and doesn’t rely on any external services. I’m curious: Would a tool like this fit in your workflow? Is storing reports in YAML in the repo reasonable? Would CI enforcement feel useful or annoying? CLI: https://bit.ly/3OgcoxQ + codereport.pulko-app.com February 19, 2026 at 12:23AM

Show HN: VisibleInAI – Check if ChatGPT recommends your brand https://bit.ly/4aBRkcj

Show HN: VisibleInAI – Check if ChatGPT recommends your brand https://bit.ly/3ZHE3KA February 18, 2026 at 12:10AM

Show HN: I built the Million Dollar Homepage for agents https://bit.ly/4tG5v9b

Show HN: I built the Million Dollar Homepage for agents https://bit.ly/4anXy0J February 17, 2026 at 02:31PM

Show HN: Andrej Karpathy's microgpt.py to C99 microgpt.c – 4,600x faster https://bit.ly/3MzBpn5

Show HN: Andrej Karpathy's microgpt.py to C99 microgpt.c – 4,600x faster Andrej Karpathy showed us the GPT algorithm. I wanted to see the hardware limit. The Punchline: I made it go 4,600x faster in pure C code, no dependencies and using a compiler with SIMD auto-vectorisation!!! Andrej recently released microgpt.py - a brilliant, atomic look at the core of a GPT. As a low-latency developer, I couldn't resist seeing how fast it could go when you get closer to the metal. So just for funzies, I spent a few hours building microgpt-c, a zero-dependency and pure C99 implementation featuring: - 4,600x Faster training vs the Python reference (Tested on MacBook Pro M2 Max). On Windows, it is 2,300x faster. - SIMD Auto-vectorisation for high-speed matrix operations. - INT8 Quantisation (reducing weight storage by ~8x). Training is slightly slower, but the storage reduction is significant. - Zero Dependencies - just pure logic. The amalgamation image below is just for fun (and to show off the density!), but the GitHub repo contains the fully commented, structured code for anyone who wants to play with on-device AI. I have started to build something useful, like a simple C code static analyser - I will do a follow-up post. Everything else is just efficiency... but efficiency is where the magic happens https://bit.ly/4rV63GC February 17, 2026 at 01:06AM