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Show HN: The Blots Programming Language

I've been working on this small, slightly weird expression-oriented programming language for a little while now and feel ready to share it with others. I use it pretty often now in my day-to-day and work life, as a scratchpad for doing a bit of quick math or picking some pieces of data out of a JSON payload.<p>Would really appreciate any feedback about the syntax, docs, features that are glaringly missing, etc. Before anybody mentions it: I know the performance is pretty lousy when dealing with a lot of data. If you can believe it, the runtime is about 100x faster than it used to be! Long term I'd like to switch to a proper bytecode interpreter, but so far performance has been Good Enough for my use cases.<p>Thanks for taking a look!

Show HN: The Blots Programming Language

I've been working on this small, slightly weird expression-oriented programming language for a little while now and feel ready to share it with others. I use it pretty often now in my day-to-day and work life, as a scratchpad for doing a bit of quick math or picking some pieces of data out of a JSON payload.<p>Would really appreciate any feedback about the syntax, docs, features that are glaringly missing, etc. Before anybody mentions it: I know the performance is pretty lousy when dealing with a lot of data. If you can believe it, the runtime is about 100x faster than it used to be! Long term I'd like to switch to a proper bytecode interpreter, but so far performance has been Good Enough for my use cases.<p>Thanks for taking a look!

Show HN: Kekkai – a simple, fast file integrity monitoring tool in Go

I built a tool called Kekkai for file integrity monitoring in production environments. It records file hashes during deployment and later verifies them to detect unauthorized modifications (e.g. from OS command injection or tampering).<p>Why it matters:<p>* Many web apps (PHP, Ruby, Python, etc.) on AWS EC2 need a lightweight way to confirm their code hasn’t been changed. * Traditional approaches that rely on metadata often create false positives. * Kekkai checks only file content, so it reliably detects real changes. * I’ve deployed it to an EC2 PHP application in production, and it’s working smoothly so far.<p>Key points:<p>* Content-only hashing (ignores timestamps/metadata) * Symlink protection (detects swaps/changes) * Secure S3 storage (deploy servers write-only, app servers read-only) * Single Go binary with minimal dependencies<p>Would love feedback from others running apps on EC2 or managing file integrity in production.

Show HN: Kekkai – a simple, fast file integrity monitoring tool in Go

I built a tool called Kekkai for file integrity monitoring in production environments. It records file hashes during deployment and later verifies them to detect unauthorized modifications (e.g. from OS command injection or tampering).<p>Why it matters:<p>* Many web apps (PHP, Ruby, Python, etc.) on AWS EC2 need a lightweight way to confirm their code hasn’t been changed. * Traditional approaches that rely on metadata often create false positives. * Kekkai checks only file content, so it reliably detects real changes. * I’ve deployed it to an EC2 PHP application in production, and it’s working smoothly so far.<p>Key points:<p>* Content-only hashing (ignores timestamps/metadata) * Symlink protection (detects swaps/changes) * Secure S3 storage (deploy servers write-only, app servers read-only) * Single Go binary with minimal dependencies<p>Would love feedback from others running apps on EC2 or managing file integrity in production.

Show HN: Kekkai – a simple, fast file integrity monitoring tool in Go

I built a tool called Kekkai for file integrity monitoring in production environments. It records file hashes during deployment and later verifies them to detect unauthorized modifications (e.g. from OS command injection or tampering).<p>Why it matters:<p>* Many web apps (PHP, Ruby, Python, etc.) on AWS EC2 need a lightweight way to confirm their code hasn’t been changed. * Traditional approaches that rely on metadata often create false positives. * Kekkai checks only file content, so it reliably detects real changes. * I’ve deployed it to an EC2 PHP application in production, and it’s working smoothly so far.<p>Key points:<p>* Content-only hashing (ignores timestamps/metadata) * Symlink protection (detects swaps/changes) * Secure S3 storage (deploy servers write-only, app servers read-only) * Single Go binary with minimal dependencies<p>Would love feedback from others running apps on EC2 or managing file integrity in production.

Show HN: Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput

Show HN: Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput

Show HN: Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput

Show HN: Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput

Show HN: Run Qwen3-Next-80B on 8GB GPU at 1tok/2s throughput

Show HN: Minecraft Villager Sounds for Terminal Errors

Show HN: Freeing GPUs stuck by runaway jobs

Show HN: Zenode – an AI-powered electronic component search engine

TL;DR - My cofounder Collin and I built an AI version of Digi-Key to help PCB designers find and use parts, except with a way bigger catalog, modern refinement tools, and an AI that can actually read the damn datasheets for you.<p>The problem<p>Modern circuit board design is filled with absurdly tedious tasks, where one small mistake can brick a project and cost thousands. The worst (in our opinion) is reading datasheets, which eats up to 25% of the first part of any project: 1. First, you slog through catalogs to find viable parts, using search tools that are still stuck in the dark ages. There are ~80M unique components in today’s supply chain, yet the tools we have to look through them are just digitized versions of the same paper catalogs our grandparents got in the mail.<p>2. During the design, you spend a ton of time flipping between different 10-100-page PDFs for every component in every subcircuit, hoping like hell you don’t miss some tiny spec in a footnote somewhere that kills your design.<p>3. And god help you when the requirements inevitably change and now you have to figure out what subsystems are affected!<p>What we built<p>Zenode is an AI-powered electronics search engine that actually helps engineers find and understand components. Our core features: 1. Largest and Deepest Part Catalog → We have merged dozens of existing part catalogs and documents from major distributors and manufacturers<p>2. Discovery Search → natural language queries to quickly find categories, set filters, and rank results<p>3. Modern Parametric Filters → rebuilt from scratch to move off the string values pervasive in industry and build numeric ranges that actually work.<p>4. Interactive Documents → AI constrained to a single part’s datasheet/manuals. Ask a question, get the answer with a highlighted source for quick reference.<p>5. Deep Dive → search across dozens of parts simultaneously (“what’s the lowest-power accelerometer available?”) instead of slogging one by one.<p>What we learned<p>1. By far the hardest part of the last 2 years has been wrangling 3 TB of messy, inconsistent data into something usable. We had to teach the AI how to handle hand-drawn figures, normalize different unit variables and names that mean the same thing, and navigate conflicting information present between different datasheet versions of the same part. It’s been a nightmare<p>2. We originally built custom PDF parsers and AI extractors, which were best in class for ~3 months until generalized AI passed them. So we stopped reinventing wheels and doubled down on data quality instead.<p>3. The killer feature wasn’t the AI searching a single part, but what we heard repeatedly from users is that they want the AI to read across multiple parts, hence why we’ve launched deep dive!<p>Where it’s strong<p>- Speed: rips through a 1,000-page microcontroller datasheet in seconds.<p>- Breadth: 40M+ part sources unified into one catalog, and more than just datasheets, application notes, errata, etc.<p>- Comparisons: Deep Dive lets you ask across multiple parts, not just one at a time.<p>Where it’s not<p>- Pricing/availability: currently outdated (for now we expect folks to check existing aggregators like Octopart).<p>- Accuracy: good enough to match my mediocre skills; not yet at Collin's level, but we're starting tuning and this will improve rapidly!<p>Try it<p>It’s live today (zenode.ai). Sign up for a free account and If you put “Hacker News” in during signup in the “where did you hear about us” field, we’ll give you 1,000 bonus credits (once we finish building that, so sometime this week ).<p>Feedback we’d love<p>1. Should Deep Dive results auto-become filters you can refine further?<p>2. Do you want the ability to mark preferred parts / exclude others?<p>3. Is “Deep Dive on a BOM” (alt discovery + manufacturability checks on a list of known components from different categories) the killer feature?

Show HN: Zenode – an AI-powered electronic component search engine

TL;DR - My cofounder Collin and I built an AI version of Digi-Key to help PCB designers find and use parts, except with a way bigger catalog, modern refinement tools, and an AI that can actually read the damn datasheets for you.<p>The problem<p>Modern circuit board design is filled with absurdly tedious tasks, where one small mistake can brick a project and cost thousands. The worst (in our opinion) is reading datasheets, which eats up to 25% of the first part of any project: 1. First, you slog through catalogs to find viable parts, using search tools that are still stuck in the dark ages. There are ~80M unique components in today’s supply chain, yet the tools we have to look through them are just digitized versions of the same paper catalogs our grandparents got in the mail.<p>2. During the design, you spend a ton of time flipping between different 10-100-page PDFs for every component in every subcircuit, hoping like hell you don’t miss some tiny spec in a footnote somewhere that kills your design.<p>3. And god help you when the requirements inevitably change and now you have to figure out what subsystems are affected!<p>What we built<p>Zenode is an AI-powered electronics search engine that actually helps engineers find and understand components. Our core features: 1. Largest and Deepest Part Catalog → We have merged dozens of existing part catalogs and documents from major distributors and manufacturers<p>2. Discovery Search → natural language queries to quickly find categories, set filters, and rank results<p>3. Modern Parametric Filters → rebuilt from scratch to move off the string values pervasive in industry and build numeric ranges that actually work.<p>4. Interactive Documents → AI constrained to a single part’s datasheet/manuals. Ask a question, get the answer with a highlighted source for quick reference.<p>5. Deep Dive → search across dozens of parts simultaneously (“what’s the lowest-power accelerometer available?”) instead of slogging one by one.<p>What we learned<p>1. By far the hardest part of the last 2 years has been wrangling 3 TB of messy, inconsistent data into something usable. We had to teach the AI how to handle hand-drawn figures, normalize different unit variables and names that mean the same thing, and navigate conflicting information present between different datasheet versions of the same part. It’s been a nightmare<p>2. We originally built custom PDF parsers and AI extractors, which were best in class for ~3 months until generalized AI passed them. So we stopped reinventing wheels and doubled down on data quality instead.<p>3. The killer feature wasn’t the AI searching a single part, but what we heard repeatedly from users is that they want the AI to read across multiple parts, hence why we’ve launched deep dive!<p>Where it’s strong<p>- Speed: rips through a 1,000-page microcontroller datasheet in seconds.<p>- Breadth: 40M+ part sources unified into one catalog, and more than just datasheets, application notes, errata, etc.<p>- Comparisons: Deep Dive lets you ask across multiple parts, not just one at a time.<p>Where it’s not<p>- Pricing/availability: currently outdated (for now we expect folks to check existing aggregators like Octopart).<p>- Accuracy: good enough to match my mediocre skills; not yet at Collin's level, but we're starting tuning and this will improve rapidly!<p>Try it<p>It’s live today (zenode.ai). Sign up for a free account and If you put “Hacker News” in during signup in the “where did you hear about us” field, we’ll give you 1,000 bonus credits (once we finish building that, so sometime this week ).<p>Feedback we’d love<p>1. Should Deep Dive results auto-become filters you can refine further?<p>2. Do you want the ability to mark preferred parts / exclude others?<p>3. Is “Deep Dive on a BOM” (alt discovery + manufacturability checks on a list of known components from different categories) the killer feature?

Show HN: Python Audio Transcription: Convert Speech to Text Locally

Show HN: Python Audio Transcription: Convert Speech to Text Locally

Show HN: Python Audio Transcription: Convert Speech to Text Locally

Show HN: Python Audio Transcription: Convert Speech to Text Locally

Show HN: Python Audio Transcription: Convert Speech to Text Locally

Show HN: Software Freelancers Contract Template

I started working as a freelancer [in Finland] a year ago and was surprised to learn that no decent contract template was available for direct assignments. There were some free contract templates available for intermediated assignments, but not for direct assignments. The "golden standard" of contract templates in Finland is an extremely heavy-handed and expensive template that costs ~500€ PER YEAR to use. Personally at the time I decided to just do a DIY contract for my first freelancing project.<p>Over time, as I got more engaged in the Finnish freelancing community, I realized that many people struggled with the same issue. After discussing this in our freelancing co-op Ohjelmistofriikit, we decided to invest both time and money into solving this problem. We decided right from the start that we were gonna open source everything and give it out for free.<p>We first developed a traditional document template in collaboration with a law firm. After that we developed a web generator that makes it easy to fill out the template. The user flow of the generator is designed to eliminate boilerplate-type work (such as hiding sections instead of showing "skip this section if condition X does not apply to you") and also to reduce mistakes users might make when editing a traditional document template (such as copypasting something incorrectly).<p>Although the legalese is designed for the Finnish jurisdiction, the contract template can be useful as an example for similar work in other jurisdictions.<p>Go ahead and draft a contract right there in your browser!