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Hacker News - Newest: "LLM"

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GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? 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Each agent receives a text description of the board state, reasons about it, and outputs a move as JSON. The game engine executes it. Introducing the Common AI Provider: LLM and AI Agent Support for Apache Airflow Power Circuit AI: Designing Power Electronic Circuits for Motor Drives with Generative Artificial Intelligence Ask HN: How to program with IDE and LLM on CPU locally? Show HN: Agent-cache – Multi-tier LLM/tool/session caching for Valkey and Redis Bonsai 1-bit WebGPU - a Hugging Face Space by webml-community The LLM Fallacy: Misattribution in AI-Assisted Cognitive Workflows Ask HN: Simple tooling for local LLM code critique without IDE integration? Can a General LLM Diagnose a DICOM Slice? A 10-Case Public Benchmark Charts-of-Thought: Enhancing LLM Visualization Literacy (PDF, 2026) GitHub - Mesh-LLM/mesh-llm: Distributed AI/LLM for the people. Share compute privately or publicly to power your agents and chat. GitHub - seamus-brady/springdrift: A persistent runtime for long-lived LLM agents Writing an LLM from scratch, part 32k -- Interventions: training a better model locally with gradient accumulation Ask HN: Which LLM model and agentic CLI are you using for local development? GitHub - wayneColt/modelcascade: Route local. Escalate smart. Never overspend. Open-source multi-model cascade routing for autonomous agents. LLM pricing is 100x harder than you think GitHub - asakin/llm-primer: Pre-warmed Claude Code sessions in tmux. No startup wait. GitHub - EggerMarc/chat-rs: A multi-provider LLM framework for Rust. GitHub - SynapseKit/SynapseKit: Minimal, async-first Python framework for production LLM apps- 2 hard deps, no magic, no SaaS. A Claude Skill that Makes LLM Paragraphs More Bearable Does Gas Town 'steal' usage from users' LLM credits & paid services to improve itself? What's Claude Code Actually Doing? 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GitHub - stfurkan/pi-llm LLM-Wiki Show HN: Formal – Formal verification for AI-generated code using Lean 4 LRTS – Regression testing for LLM prompts (open source, local-first) LLM Wiki Skill: Build a Second Brain with Claude Code and Obsidian I built an LLM Wiki and RAG solution: here's a demo for a security KB The biggest advance in AI since the LLM Predict-Rlm: The LLM Runtime That Lets Models Write Their Own Control Flow the-synthetic-library/the-synthetic-mind at main · joshferrer1/the-synthetic-library GitHub - yisding/reviewwiggum GitHub - Donnyb369/mcp-spine: Context Minifier & State Guard — Local-first MCP middleware proxy GitHub - Beledarian/wgpu-llm: A from-scratch LLM inference engine that uses wgpu (the cross-platform WebGPU implementation) to dispatch WGSL compute shaders for every math operation a Transformer needs. No CUDA. No Python. No massive framework dependencies. Just Rust, raw shaders, and your GPU. GitHub - anitiue/Hindsight: An experience-driven self-improvement framework for LLM agents — 基于经验的 LLM Agent 自我改进框架 GitHub - stef41/lmscan: 🔍 Detect AI-generated text and fingerprint which LLM wrote it. Open-source GPTZero alternative. Zero dependencies, works offline. GitHub - alainnothere/AmdPerformanceTesting: Amd Performance Testing Ask HN: Is a purely Markdown-based CRM a terrible idea? Optimized for LLM agents Context Engineering - LLM Memory and Retrieval for AI Agents | Weaviate little_helper_tui/letter.md at main · sleepyeldrazi/little_helper_tui GitHub - EvanZhouDev/umr: The Unified Model Registry for all your local AI apps. GitHub - JordanCT/VigIA-Orchestrator Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain A Taxonomy of RL Environments for LLM Agents Llama LLM Network Feture GitHub - genedeng-ca/ai-mac-migration: AI-powered Mac-to-Mac migration tool - replace Apple Migration Assistant with intelligent, selective transfer using local LLMs GitHub - lunargate-ai/gateway: High-performance self-hosted AI gateway (OpenAI-compatible) with routing, retries, and streaming GitHub - AuthBits/webmcp: A lightweight, prompt-driven MCP web research server for high-quality LLM powered information extraction. Externalization in LLM Agents: A Unified Review of Memory, Skills, Protocols and Harness Engineering Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception High-Stakes Personalization: Rethinking LLM Customization for Individual Investor Decision-Making From Static Templates to Dynamic Runtime Graphs: A Survey of Workflow Optimization for LLM Agents HUOZIIME: An On-Device LLM-enhanced Input Method for Deep Personalization TIDE: Token-Informed Depth Execution for Per-Token Early Exit in LLM Inference Characterizing WebGPU Dispatch Overhead for LLM Inference Across Four GPU Vendors, Three Backends, and Three Browsers LLM Targeted Underperformance Disproportionately Impacts Vulnerable Users
GitHub - ojuschugh1/sqz: Compress LLM context to save tokens and reduce costs
sea-gold · 2026-04-23 · via Hacker News - Newest: "LLM"
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Compress LLM context to save tokens and reduce costs

Real session stats: 3,003 compressions · 178,442 tokens saved · 24.7% avg reduction · up to 92% with dedup

Crates.io npm PyPI VS Code Firefox JetBrains Discord

Install · How It Works · Supported Tools · Changelog · Discord


sqz compresses command output before it reaches your LLM. Single Rust binary, zero config.

The real win is dedup: when the same file gets read 5 times in a session, sqz sends it once and returns a 13-token reference for every repeat.

Without sqz:                    With sqz:

File read #1:  2,000 tokens     File read #1:  ~800 tokens (compressed)
File read #2:  2,000 tokens     File read #2:  ~13 tokens  (dedup ref)
File read #3:  2,000 tokens     File read #3:  ~13 tokens  (dedup ref)
───────────────────────         ───────────────────────
Total:         6,000 tokens     Total:         ~826 tokens (86% saved)

Token Savings

24.7% average reduction across 3,003 real compressions · 92% saved on repeated file reads · 86% on shell/git output · 13-token refs for cached content

One developer's week, measured from actual sqz gain output:

$ sqz gain
sqz token savings (last 7 days)
──────────────────────────────────────────────────
  04-13 │                              │   2,329 saved
  04-14 │                              │       0 saved
  04-15 │███                           │  12,954 saved
  04-16 │██                            │   9,223 saved
  04-17 │████                          │  14,752 saved
  04-18 │██████████████████████████████│ 105,569 saved
  04-19 │████████                      │  30,882 saved
  04-20 │█                             │   4,334 saved
──────────────────────────────────────────────────
  Total: 3,003 compressions, 178,442 tokens saved (24.7% avg reduction)

Per-command compression

Single-command compression (measured via cargo test -p sqz-engine benchmarks):

Content Before After Saved
Repeated log lines 148 62 58%
Large JSON array 259 142 45%
JSON API response 64 53 17%
Git diff 61 54 12%
Prose/docs 124 121 2%
Stack trace (safe mode) 82 82 0%

Session-level with dedup

Where the real savings live — the cache sends each file once, repeats cost 13 tokens:

Scenario Without sqz With sqz Saved
Same file read 5× 10,000 826 92%
Same JSON response 3× 192 79 59%
Test-fix-test cycle (3 runs) 15,000 5,186 65%

Single-command compression ranges from 2–58% depending on content. Repeated reads drop to 13 tokens each. Your mileage will vary with how repetitive your tool calls are — agentic sessions with many file re-reads see the biggest wins.

Install

Prebuilt binaries (no compiler required — works on every platform):

# macOS / Linux
curl -fsSL https://raw.githubusercontent.com/ojuschugh1/sqz/main/install.sh | sh

# Windows (PowerShell)
irm https://raw.githubusercontent.com/ojuschugh1/sqz/main/install.ps1 | iex

# Any platform via npm
npm install -g sqz-cli

Build from source (cargo install sqz-cli) works too, but needs a C toolchain:

  • Linux: build-essential (apt) or equivalent
  • macOS: Xcode Command Line Tools (xcode-select --install)
  • Windows: Visual Studio Build Tools with the "Desktop development with C++" workload. Without these, cargo install fails with linker link.exe not found. If you don't already have them, use the PowerShell or npm install above instead.

Then initialize:

sqz init --global     # hooks apply to every project on this machine
# or
sqz init              # hooks apply to just this project (.claude/settings.local.json)

--global writes to ~/.claude/settings.json (the user scope per the Anthropic scope table), so the sqz hook fires in every Claude Code session on this machine. This is the common case on first install. Your existing permissions, env, statusLine, and unrelated hooks in ~/.claude/settings.json are preserved — sqz merges its entries rather than overwriting.

Plain sqz init (project scope) is useful when you want sqz active only inside one repo.

Only using one agent? Pass --only (or --skip) to limit which configs are written:

sqz init --only opencode              # just OpenCode, nothing else
sqz init --only opencode,codex        # OpenCode and Codex
sqz init --skip cursor,windsurf       # everything except Cursor and Windsurf

Accepted names: claude, cursor, windsurf, cline, gemini, opencode, codex. Aliases (claude-code, gemini-cli, roo) also work. --only and --skip can't be combined.

That's it. Shell hooks installed, AI tool hooks configured.

How It Works

sqz installs a PreToolUse hook that intercepts bash commands before your AI tool runs them. The output gets compressed transparently — the AI tool never knows.

Claude → git status → [sqz hook rewrites] → compressed output (85% smaller)

What gets compressed:

  • Shell output — git, cargo, npm, docker, kubectl, ls, grep, etc.
  • JSON — strips nulls, compact encoding
  • Logs — collapses repeated lines
  • Test output — shows failures only

What doesn't get compressed:

  • Stack traces, error messages, secrets — routed to safe mode (0% compression)
  • Your prompts and the AI's responses — controlled by the AI tool, not sqz

Supported Tools

Tool Integration Setup
Claude Code PreToolUse hook (transparent) sqz init
Cursor PreToolUse hook (transparent) sqz init
Windsurf PreToolUse hook (transparent) sqz init
Cline PreToolUse hook (transparent) sqz init
Gemini CLI BeforeTool hook (transparent) sqz init
OpenCode TypeScript plugin (transparent) sqz init
VS Code Extension Install from Marketplace
JetBrains Plugin Install from Marketplace
Chrome Browser extension ChatGPT, Claude.ai, Gemini, Grok, Perplexity
Firefox Browser extension Same sites

CLI

sqz init --global             # Install hooks for every project on this machine
sqz init                      # Install hooks for just this project
sqz init --only opencode      # Only configure OpenCode (skip the rest)
sqz init --skip cursor        # Configure every agent except Cursor
sqz compress <text>           # Compress (or pipe from stdin)
sqz compress --no-cache       # Compress without dedup (always full output)
sqz expand <ref>              # Recover original content from a §ref:HASH§ token
sqz compact                   # Evict stale context to free tokens
sqz gain                      # Show daily token savings
sqz stats                     # Cumulative report
sqz discover                  # Find missed savings
sqz resume                    # Re-inject session context after compaction
sqz hook claude               # Process a PreToolUse hook
sqz proxy --port 8080 # API proxy (compresses full request payloads)

Dedup Escape Hatch

When sqz sees the same content twice, it returns a compact §ref:HASH§ token instead of the full text. Most models handle this fine, but some (e.g., GLM 5.1) can't parse the ref format and loop. Four ways to work around this:

# 1. Recover original content from a ref
sqz expand a1b2c3d4              # prefix match
sqz expand '§ref:a1b2c3d4§'     # paste the whole token

# 2. Compress without dedup (per-invocation)
echo "..." | sqz compress --no-cache

# 3. Disable dedup globally (env var)
export SQZ_NO_DEDUP=1

# 4. MCP passthrough tool (returns input byte-exact, zero transforms)
# Available via tools/list when sqz-mcp is running

Track Your Own Savings

Run sqz gain in your shell any time to see your own daily breakdown (see the Token Savings section above for what the output looks like), and sqz stats for the full cumulative report:

$ sqz stats
┌─────────────────────────┬──────────────────┐
│           sqz compression stats            │
├─────────────────────────┼──────────────────┤
│ Total compressions      │            3,003 │
│ Tokens saved            │          178,442 │
│ Avg reduction           │            24.7% │
│ Cache entries           │               43 │
│ Cache size              │          39.1 KB │
└─────────────────────────┴──────────────────┘

Stats are stored locally in SQLite under ~/.sqz/sessions.db — nothing leaves your machine.

How Compression Works

  1. Per-command formattersgit status → compact summary, cargo test → failures only, docker ps → name/image/status table
  2. Structural summaries — code files compressed to imports + function signatures + call graph (~70% reduction). The model sees the architecture, not implementation noise.
  3. Dedup cache — SHA-256 content hash, persistent across sessions. Second read = 13-token reference.
  4. JSON pipeline — strip nulls → project out debug fields → flatten → collapse arrays → TOON encoding (lossless compact format)
  5. Safe mode — stack traces, secrets, migrations detected by entropy analysis and routed through with 0% compression

For the full technical details, see docs/.

Configuration

# ~/.sqz/presets/default.toml
[preset]
name = "default"
version = "1.0"

[compression.condense]
enabled = true
max_repeated_lines = 3

[compression.strip_nulls]
enabled = true

[budget]
warning_threshold = 0.70
default_window_size = 200000

Privacy

  • Zero telemetry — no data transmitted, no crash reports
  • Fully offline — works in air-gapped environments
  • All processing local

Development

git clone https://github.com/ojuschugh1/sqz.git
cd sqz
cargo test --workspace
cargo build --release

License

Elastic License 2.0 (ELv2) — use, fork, modify freely. Two restrictions: no competing hosted service, no removing license notices.

Links

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