Show HN: I made FiscalBud to send invoices fast and worldwide in 77 languages https://ift.tt/Gbpmeg9

Show HN: I made FiscalBud to send invoices fast and worldwide in 77 languages hi! i built an app that takes the pain out of invoicing so you can send them faster and worldwide without a headache. i've always found invoicing to be a waste of time, switching between templates, calculating taxes, tracking different currencies, and keeping files organized. so i made FiscalBud :) the idea from tools like stripe inspired me, but for invoices. it lets you create, customize, and send professional invoices to clients anywhere in the world in just minutes. it supports 8 currencies, 77 languages (you can choose the output data language and ui language separately), and works in 248 countries, so you can bill confidently on a global scale. it comes with smart templates, automatic tax/subtotal/total calculations, localized csv exports, and cloud storage to keep everything organized. (coming soon) you can automate recurring invoices, payment reminders, and follow-ups. it's built to be secure and privacy-focused, with encryption and compliance baked in. you can even send invoices directly via email using your own smtp settings, with automatically signed pdfs. i've got plenty of ideas for making it even better, like deeper automation and more integrations with other tools you already use (including Stripe which is on the roadmap). any feedback is much appreciated! :) https://ift.tt/dzgFGsm August 9, 2025 at 02:56AM

Show HN: From Hacking a T480 to the Fastest Open-Hardware 75 Hz E-Ink Display https://ift.tt/Q8FA6bM

Show HN: From Hacking a T480 to the Fastest Open-Hardware 75 Hz E-Ink Display Three years ago, I posted here about hacking together a fast e-ink laptop from a T480 because I was tired of spending all day on LCDs. I liked e-ink’s comfort, but it was too slow for day-to-day use. https://ift.tt/lw1hXFt That post drew in people, which grew into a community experimenting with ways to make e-ink usable for everyday computing. That project later turned into a company and a multi-year project to make e-ink fast and open. We built our own FPGA-based controller, Caster, and went through multiple iterations to push past e-ink’s usual limits, slow refresh, ghosting, and proprietary controllers. Now, after three years, we’ve launched the Modos Paper Developer Kit and Monitor: the fastest open-hardware e-ink display, with 75 Hz refresh and sub-100 ms latency. It works with 6" to 13.3" mono or color panels over HDMI or USB-C, supports multiple grayscale modes, and has a C API for low-level control. The hardware, firmware, and schematics are on our GitHub. https://ift.tt/rvkeFwR Our goal is to make e-ink fast and open enough that anyone can build on it, for hacking, research, or daily use. Thanks, HN, for being part of the journey. https://ift.tt/7KakLus August 8, 2025 at 06:24AM

Show HN: A light GPT-5 vs. Claude Code comparison https://ift.tt/pEqX1R3

Show HN: A light GPT-5 vs. Claude Code comparison Hi HN! Can’t believe I’ve been here over 12 years and this is my first Show HN. I guess this is two fold, One: I’m doing another startup! Charlie is an agent for TypeScript teams focusing heavily on augmentation. :) Two: Over the last week or so we put GPT-5 (through our Charlie Agent) head-to-head with Claude Code/Opus on 10 real TypeScript issues pulled from active OSS projects. Our Results GPT-5 beat Claude Code on all 10 case-by-case comparisons. Pull requests generated by GPT-5 resolved 29% more issues than o3. PR review quality rose 5% versus o3. Head-to-head case study We measured testability, description, and overall quality across 10 head-to-head PRs. Testability measures how thoroughly a code change is exercised by meaningful, behavior-focused tests. It considers whether tests are present and aligned with the diff, whether they explore edge cases and real-world scenarios, and whether they avoid vacuous, misleading, or implementation-dependent patterns common in code generated by LLMs. Description evaluates how clearly and accurately a pull request’s title and summary convey the purpose, scope, and structure of the code change. It emphasizes technical correctness, relevance to the diff, and clarity for future readers — penalizing vague, verbose, or hallucinated explanations often produced by code-generating agents. Quality assesses the substance and craftsmanship of the code change itself — judging whether it is correct, minimal, idiomatic, and free from hallucinated constructs. It emphasizes clarity, alignment with project norms, and logical integrity, while identifying agent-specific pitfalls like over-engineering, incoherent abstractions, or invented utilities. Testability: Charlie (0.69) vs Claude (0.55) Description: Charlie (0.84) vs Claude (0.90) Overall Quality: Charlie (0.84) vs Claude (0.65) Caveats Single-shot runs; no human feedback loop. Quality score uses a secondary LLM reviewer—subjective but transparent. Def looking for feedback on more evaluations we can do, also please do nit-pick the prompts, ideas, harness design etc etc. Tell us if this bar (CI + types) is the right one, or what you’d track instead. On a personal note: I’ve spent my career working on tools to help creators create, I’m extremely passionate about enabling people to do more easily. I am still somewhat uneasy about Gen AI, however I do believe the future is bright, certainly things are going to change - I would encourage you all to stay optimistic builders. Thanks for taking a look! https://ift.tt/v7CyAlg August 8, 2025 at 12:26AM

Show HN: Octofriend, a cute coding agent that can swap between GPT-5 and Claude https://ift.tt/C3W7HBi

Show HN: Octofriend, a cute coding agent that can swap between GPT-5 and Claude Hey HN! We're shipping Octofriend today, a cute coding assistant that can swap between GPT-5, Claude, local or open-source LLMs, etc mid-conversation as needed. It handles reasoning tokens (including encrypted ones from OpenAI and Anthropic) really well, and includes a couple of custom-trained ML models to fix minor diff edit and JSON encoding errors that we've also open-sourced. Have fun! https://ift.tt/ACoQwqL August 8, 2025 at 12:04AM

Show HN: CSV Mail Sender – Send personalized email campaigns from a CSV https://ift.tt/mEPd31t

Show HN: CSV Mail Sender – Send personalized email campaigns from a CSV https://ift.tt/Vbl3CI1 August 7, 2025 at 03:58AM

Show HN: When is the next Caltrain? (minimal webapp) https://ift.tt/yd154ks

Show HN: When is the next Caltrain? (minimal webapp) I was frustrated with the existing caltrain websites / apps, so I made a super minimalist one to answer the actual question I have: how long until the next train? If you're in SF it grabs the next southbound trains, otherwise, the next northbound. https://ift.tt/RVkgJPh August 6, 2025 at 09:20PM

Show HN: Write lead sheets in a Markdown way and transpose in a second https://ift.tt/UGySW8H

Show HN: Write lead sheets in a Markdown way and transpose in a second Hey HN, I'm a software engineer with a passion for playing guitar. ( https://ivanhsu.co ) In the software industry, we use clever plain-text syntaxes like Markdown and Mermaid to handle complex layouts. This lets us focus on the content itself and quickly produce beautifully formatted documents. Isn't sheet music and chord charts just another form of documentation in the world of music? That's why I created Cord Land https://ift.tt/JptWL4C ! It's a website where you can quickly generate lead sheets and draw chord charts using plain text. Even better, it can automatically transpose songs! Just write in one key, and it can be instantly converted it to any of the other 11 keys you want. I've implemented a new syntax called Corduroy, an extension of ChordPro syntax specifically designed for guitarists. Besides showing chord names above lyrics, you can also customize chord charts. For example, `%x32o1o%` will automatically draw a C major chord in the first position! Feel free to try it out here: https://ift.tt/oX12nv5 For more usage details, please refer to: https://ift.tt/g9hGVLU The name "Cord Land" comes from "Cord" and "Chord" being homophones, representing chords. Let's keep our passion for playing guitar alive, even after work! Ivan Hsu https://ift.tt/JptWL4C August 3, 2025 at 08:08PM

Show HN: AsyncStatus CLI – post status updates from your terminal (open-source) https://ift.tt/EXhSstT

Show HN: AsyncStatus CLI – post status updates from your terminal (open-source) Looking for early users: we ship same-day on feature requests and can adapt the tool to your workflow fast. Try it and let us know what’s missing. Happy to make it work for your team. Curious what HN thinks about the UX, install friction, or any must-have features before ditching the daily standup. https://ift.tt/Xch1ELZ August 7, 2025 at 01:24AM

Show HN: Built a fast, no-fluff CRM built for startups who want to close https://ift.tt/aWECVO2

Show HN: Built a fast, no-fluff CRM built for startups who want to close https://leadchee.com August 5, 2025 at 11:26PM

Show HN: I've been building an ERP for manufacturing for the last 3 years https://ift.tt/2dLJmw4

Show HN: I've been building an ERP for manufacturing for the last 3 years https://ift.tt/2fmAUMv August 5, 2025 at 03:54AM

Show HN: I made a competitive debating game(like chess.com but for debating) https://ift.tt/ilbhaOc

Show HN: I made a competitive debating game(like chess.com but for debating) Got tired of my debates with my friend's ending in "I'm right bc I said so" so I made a platform where you can debate with your friend's(or a bot, recently added feature) about whatever you want, and after the debate is done a LLM judges who's more sound in logic. Gain points and climb the leaderboard! Feedback and criticism would be appreciated(there's a discord in there if u wanna talk more in depth) https://ift.tt/JPfxIbn August 5, 2025 at 02:37AM

Show HN: FFlags – Feature flags as code, served from the edge https://ift.tt/sitNzUq

Show HN: FFlags – Feature flags as code, served from the edge Hi HN, I'm the creator of FFlags. I built this because I wanted a feature flagging system that gave me the performance and reliability of an enterprise-scale solution without the months of dev time or the vendor lock-in. The core ideas are: 1. Feature Flags as Code: You define your flag logic in TypeScript. This lets you write complex rules, which felt more natural as a developer myself than using a complex UI for logic. 2. Open Standard: The platform is built on the OpenFeature standard (specifically the Remote Evaluation Protocol). The goal is to avoid vendor lock-in and the usual enterprise slop. You're not tied to my platform if you want to move. 3. Performance: It uses an edge network to serve the flags, which keeps the wall-time latency low (sub-25ms) for globally distributed applications. I was trying to avoid the heavy cost and complexity of existing enterprise tools while still getting better performance than a simple self-hosted solution. There's a generous free tier ($39 per million requests after that, with no flag/user limits). I'm looking for feedback on the developer experience, the "flags-as-code" approach, and any technical questions you might have. Thanks for taking a look. https://fflags.com August 5, 2025 at 12:43AM

Show HN: A tiny reasoning layer that steadies LLM outputs (MIT; +22.4% accuracy) https://ift.tt/m92Xz8T

Show HN: A tiny reasoning layer that steadies LLM outputs (MIT; +22.4% accuracy) We kept shipping “simple” LLM features that were fluent-but-wrong. After too many postmortems we wrote down the failure patterns and added a small reasoning layer in front of the model. It’s model-agnostic, sits beside your existing stack, and you can implement it from a single PDF (MIT). What’s inside the PDF A problem map of 16 failure modes we kept hitting in real systems (OCR/layout drift, table-to-question mismatches, embedding≠meaning, pre-deploy collapse, etc.). Four lightweight gates you can add today: Knowledge-boundary canaries (empty/adversarial/known-fact probes). ΔS “semantic jump” check to catch fluent nonsense when the draft answer drifts from retrieved context. Layout-aware anchoring so chunking across PDFs/tables doesn’t silently break routing. A minimal semantic trace for incident review (tiny, not full transcripts). Bench snapshot (same model, with vs. without gates): Semantic Accuracy ↑ 22.4% · Reasoning Success Rate ↑ 42.1% · Stability ↑ 3.6×. Traction (last ~50 days) ~2,400 downloads of the PDF. ~300 cold GitHub stars on related material (no marketing burst). Also received a star from the creator of tesseract.js, which was nice validation from the OCR world. Why this might be useful to you You don’t need to swap models or vendors. The PDF describes checks you can drop into any RAG/agent/service pipeline. No servers, SDKs, or proxy layers—just logic you can copy. Link is Git Repo Happy to answer HN-style questions (what breaks, where it fails, ablations, how we compute ΔS, etc.). If you try it and it doesn’t help, I’m also interested in the counter-examples. with Terrseract (OCR legend) starred it verify it, we are WFFY on top1 https://ift.tt/uyj08C5 https://ift.tt/qfzKWA5 August 4, 2025 at 08:38PM

Show HN: Spatial Web Browser Engine https://ift.tt/h3siOpM

Show HN: Spatial Web Browser Engine https://m-creativelab.github.io/jsar-runtime/ August 4, 2025 at 12:58AM

Show HN: Enforce TDD in Claude Code https://ift.tt/SmaRgb5

Show HN: Enforce TDD in Claude Code https://ift.tt/NCQpzrh August 3, 2025 at 10:55PM

Show HN: Fast Elevation API with memory mapped tiles https://ift.tt/suKxOBC

Show HN: Fast Elevation API with memory mapped tiles I recently wrote and launched a high-performance Elevation API, built from the ground up, in C. I was highly inspired by the handmade community and I was intrigued by the idea of handling fairly large datasets and optimizing caching and smart prefetching, and to cream out maximum performance in terms of latency and handling large loads. The whole thing is built from scratch. I wanted to roll my own high performance server that could handle a lot, mostly for the technical challenge but also because it brings down hosting costs. At the core is a hand made TCP server where a single thread handles all I/O via epoll, distributing the events to a pool of worker threads. The server is fully non-blocking and edge-triggered, with a minimal syscall footprint during steady-state operation. Worker threads handle request parsing and perform either direct elevation lookups for single- or multi-points, or compute sample points along polyline paths. The elevation data is stored as memory mapped geotiff raster tiles, The tiles are indexed in an R-tree for fast lookup. Given a coordinate, the correct tile is located with a bounding-box search algorithm through the tree, and the elevation value is extracted directly from the mapped memory. If the tile is missing the data, underlying tiles act as fallback. I also implemented a prefetching mechanism. That is, to avoid repeated page faults in popular areas, I employ a strategy where each tile is divided into smaller sub-tiles. Then, I have a running popularity count per sub-tile. This information is then used to guide prefetching. More popular sub-tiles trigger larger-radius prefetches around the lookup point, with the logic that if a specific region is seeing frequent access, it’s worth pulling in more of it into RAM. Over time, this makes the memory layout adapt to real usage patterns, keeping hot areas resident and minimizing I/O latency. Prefetching is done using linux madvise, in a separate prefetch thread to not affect request latency. There’s a free option to try it out! https://ift.tt/x0NsTu2 August 3, 2025 at 02:42AM

Show HN: Open-sourced my prompt management tool for LLM-powered apps https://ift.tt/4vClhqD

Show HN: Open-sourced my prompt management tool for LLM-powered apps https://ift.tt/9wY0mk7 August 3, 2025 at 01:42AM

Show HN: WebGPU enables local LLM in the browser – demo site with AI chat https://ift.tt/3P1DTwH

Show HN: WebGPU enables local LLM in the browser – demo site with AI chat Browser LLM demo working on JavaScript and WebGPU. WebGPU is already supported in Chrome, Safari, Firefox, iOS (v26) and Android. Demo, similar to ChatGPT https://andreinwald.github.io/browser-llm/ Code https://ift.tt/IbnFYD1 - No need to use your OPENAI_API_KEY - its local model that runs on your device - No network requests to any API - No need to install any program - No need to download files on your device (model is cached in browser) - Site will ask before downloading large files (llm model) to browser cache - Hosted on Github Pages from this repo - secure, because you see what you are running https://andreinwald.github.io/browser-llm/ August 2, 2025 at 07:39PM

Show HN: List of Clojure-Like projects https://ift.tt/1PqyRdW

Show HN: List of Clojure-Like projects https://ift.tt/0tLoXIR August 2, 2025 at 01:47AM

Show HN: TraceRoot – Open-source agentic debugging for distributed services https://ift.tt/HncIgSm

Show HN: TraceRoot – Open-source agentic debugging for distributed services Hey Xinwei and Zecheng here, we are the authors of TraceRoot ( https://ift.tt/y0eF5RN ). TraceRoot ( https://traceroot.ai ) is an open-source debugging platform that helps engineers fix production issues faster by combining structured traces, logs, source code contexts and discussions in Github PRs, issues and Slack channels, etc. with AI Agents. At the heart are our lightweight Python ( https://ift.tt/ImpzvVF ) and TypeScript ( https://ift.tt/g27QUJT ) SDKs - they can hook into your app using OpenTelemetry and captures logs and traces. These are either sent to a local Jaeger ( https://ift.tt/nrh4yW5 ) + SQLite backend or to our cloud backend, where we correlate them into a single view. From there, our custom agent takes over. The agent builds a heterogeneous execution tree that merges spans, logs, and GitHub context into one internal structure. This allows it to model the control and data flow of a request across services. It then uses LLMs to reason over this tree - pruning irrelevant branches, surfacing anomalous spans, and identifying likely root causes. You can ask questions like “what caused this timeout?” or “summarize the errors in these 3 spans”, and it can trace the failure back to a specific commit, summarize the chain of events, or even propose a fix via a draft PR. We also built a debugging UI that ties everything together - you explore traces visually, pick spans of interest, and get AI-assisted insights with full context: logs, timings, metadata, and surrounding code. Unlike most tools, TraceRoot stores long-term debugging history and builds structured context for each company - something we haven’t seen many others do in this space. What’s live today: - Python and TypeScript SDKs for structured logs and traces. - AI summaries, GitHub issue generation, and PR creation. - Debugging UI that ties everything together TraceRoot is MIT licensed and easy to self-host (via Docker). We support both local mode (Jaeger + SQLite) and cloud mode. Inspired by OSS projects like PostHog and Supabase - core is free, enterprise features like agent mode multi-tenant and slack integration are paid. If you find it interesting, you can see a demo video here: https://www.youtube.com/watch?v=nb-D3LM0sJM We’d love you to try TraceRoot ( https://traceroot.ai ) and share any feedback. If you're interested, our code is available here: https://ift.tt/y0eF5RN . If we don’t have something, let us know and we’d be happy to build it for you. We look forward to your comments! https://ift.tt/y0eF5RN August 1, 2025 at 10:28PM