Show HN: Ten years of running every day, visualized
Show HN (score: 14)Description
Running has truly changed my life: I've made lifelong friends, explored beautiful places, and more importantly invested into my own health and fitness, which I'm starting to see the positive benefits as I get older.
The stack is pretty simple: a NextJS app, with a Postgres database to keep all my running data, and all the stats are pre-computed and cached in Redis, so I effectively only hit the database once a day when a new run is ingested. On the fronted, I toyed with the idea of using D3 or pre-existing data viz libraries, but ended up rolling my own using SVGs directly, it gave me more control on the visualizations.
I used the Strava bulk export to pre-populate the database, and I'm using their webhook API to do incremental updates. I have to tap into OpenWeatherMap and OpenCageDate to enrich the running data a little bit.
Happy to answer anything about the stack, data pipeline, or how I stayed motivated for 10 years!
[1] https://www.runeveryday.com Run Streak Association rules: ≥ 1 mile per day
More from Show
Show HN: Grapes Studio – HTML-first WYSIWYG website editor with LLM assistant
Show HN: Grapes Studio – HTML-first WYSIWYG website editor with LLM assistant I’ve been working with @artf (creator of GrapesJS) on Grapes Studio, an HTML-first editor with an LLM assistant on top of GrapesJS.<p>We’re approaching this differently than the new wave of AI app/site builders which are typically generating full React applications, which we think is overkill for simple websites. From talking to people using these tools, we’ve seen a lot of issues with build errors and overly complicated pages.<p>With our approach you can:<p>- Edit visually via the no-code editor (drag/drop) or ask the LLM to make scoped changes (like “add a section” or “add a new page”).<p>- Build with straight HTML/CSS<p>- Ask AI to import your current site and start building from there instead of total rebuild.<p>We think there’s a lot of benefit using drag and drop editor functionality with LLMs, or you can jump straight into the code in the editor if you choose.<p>- Do you see value in this hybrid model (AI + visual + code editing)?<p>- What are the biggest blockers you’ve run into with AI-only builders?<p>Let us know what you think.
Show HN: Nanobot – Turn MCP servers into full AI agents
Show HN: Nanobot – Turn MCP servers into full AI agents Today we're releasing Nanobot an open-source framework for building AI agents on top of the Model Context Protocol (MCP).<p>MCP servers are a great way to expose structured tools, but they’re usually just that—collections of functions. Nanobot makes it simple to wrap any MCP server with reasoning, a system prompt, and orchestration so it behaves like a real agent. Even better, Nanobot fully supports MCP-UI, so agents can pass rich interactive components (forms, dashboards, even mini-apps) directly into chat.<p>A simple example: if you had a Blackjack MCP server with tools like deal, bet, and hit, you could wrap it with Nanobot to create a dealer agent that knows how to explain the game, guide a player, and render an interactive Blackjack table inside chat.<p>We built this because we wanted agents that go beyond text and function calls, into actual interactive experiences—something useful for everything from games to e-commerce to developer tools.<p>Code is on GitHub: <a href="https://github.com/nanobot-ai/nanobot" rel="nofollow">https://github.com/nanobot-ai/nanobot</a><p>Live demo (Blackjack): <a href="https://blackjack.nanobot.ai" rel="nofollow">https://blackjack.nanobot.ai</a><p>We’d love feedback from this community—on the framework, the design, and what you’d like to see next.
Show HN: Fine-tuned Llama 3.2 3B to match 70B models for local transcripts
Show HN: Fine-tuned Llama 3.2 3B to match 70B models for local transcripts I wrote a small local tool to transcribe audio notes (Whisper/Parakeet). Code: <a href="https://github.com/bilawalriaz/lazy-notes" rel="nofollow">https://github.com/bilawalriaz/lazy-notes</a><p>I wanted to process raw transcripts locally without OpenRouter. Llama 3.2 3B with a prompt was decent but incomplete, so I tried SFT. I fine-tuned Llama 3.2 3B to clean/analyze dictation and emit structured JSON (title, tags, entities, dates, actions).<p>Data: 13 real memos → Kimi K2 gold JSON → ~40k synthetic + gold; keys canonicalized. Chutes.ai (5k req/day).<p>Training: RTX 4090 24GB, ~4h, LoRA (r=128, α=128, dropout=0.05), max seq 2048, bs=16, lr=5e-5, cosine, Unsloth. On 2070 Super 8GB it was ~8h.<p>Inference: merged to GGUF, Q4_K_M (llama.cpp), runs in LM Studio.<p>Evals (100-sample, scored by GLM 4.5 FP8): overall 5.35 (base 3B) → 8.55 (fine-tuned); completeness 4.12 → 7.62; factual 5.24 → 8.57.<p>Head-to-head (10 samples): ~8.40 vs Hermes-70B 8.18, Mistral-Small-24B 7.90, Gemma-3-12B 7.76, Qwen3-14B 7.62. Teacher Kimi K2 ~8.82.<p>Why: task specialization + JSON canonicalization reduces variance; the model learns the exact structure/fields.<p>Lessons: train on completions only; synthetic is fine for narrow tasks; Llama is straightforward to train. Dataset pipeline + training script + evals: <a href="https://github.com/bilawalriaz/local-notes-transcribe-llm" rel="nofollow">https://github.com/bilawalriaz/local-notes-transcribe-llm</a>
Show HN: Claudable – OpenSource Lovable that runs locally with Claude Code
Show HN: Claudable – OpenSource Lovable that runs locally with Claude Code Hey, HN! I'm Aaron. I built an open-source Lovable for Claude Code users.<p>Platforms like Lovable, Replit Agent, and Bolt require separate API keys and $25+/month subscriptions. But if you’re already subscribed to Claude Pro or Cursor, you can use those plans directly without extra costs.<p>Claudable runs entirely locally through Claude Code (Cursor CLI also supported) and provides:<p>- Instant UI preview (similar to Lovable)<p>- Web-optimized, production-ready designs<p>- Direct Git integration<p>- One-click Vercel deployment<p>- Zero additional API costs<p>It’s open source and available today. I’m actively developing it and would love community feedback on what features to prioritize next.<p>GitHub: <a href="https://github.com/opactorai/Claudable" rel="nofollow">https://github.com/opactorai/Claudable</a><p>Happy to answer any questions!
No other tools from this source yet.