The best Hacker News stories from Show from the past day
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Show HN: Evidex – AI Clinical Search (RAG over PubMed/OpenAlex and SOAP Notes)
Hi HN,<p>I’m a solo dev building a clinical search engine to help my wife (a resident physician) and her colleagues.<p>The Problem: Current tools (UpToDate/OpenEvidence) are expensive, slow, or increasingly heavy with pharma ads.<p>The Solution: I built Evidex to be a clean, privacy-first alternative. Search Demo (GIF): <a href="https://imgur.com/a/zoUvINt" rel="nofollow">https://imgur.com/a/zoUvINt</a><p>Technical Architecture (Search-Based RAG): Instead of using a traditional pre-indexed vector database (like Pinecone) which can serve stale data, I implemented a Real-time RAG pattern:<p>Orchestrator: A Node.js backend performs "Smart Routing" (regex/keyword analysis) on the query to decide which external APIs to hit (PubMed, Europe PMC, OpenAlex, or ClinicalTrials.gov).<p>Retrieval: It executes parallel fetches to these APIs at runtime to grab the top ~15 abstracts.<p>Local Data: Clinical guidelines are stored locally in SQLite and retrieved via full-text search (FTS) ensuring exact matches on medical terminology.<p>Inference: I’m using Gemini 2.5 Flash to process the concatenated abstracts. The massive context window allows me to feed it distinct search results and force strict citation mapping without latency bottlenecks.<p>Workflow Tools (The "Integration"): I also built a "reasoning layer" to handle complex patient histories (Case Mode) and draft documentation (SOAP Notes). Case Mode Demo (GIF): <a href="https://imgur.com/a/h01Zgkx" rel="nofollow">https://imgur.com/a/h01Zgkx</a> Note Gen Demo (GIF): <a href="https://imgur.com/a/DI1S2Y0" rel="nofollow">https://imgur.com/a/DI1S2Y0</a><p>Why no Vector DB? In medicine, "freshness" is critical. If a new trial drops today, a pre-indexed vector store might miss it. My real-time approach ensures the answer includes papers published today.<p>Business Model: The clinical search is free. I plan to monetize by selling billing automation tools to hospital admins later.<p>Feedback Request: I’d love feedback on the retrieval latency (fetching live APIs is slower than vector lookups) and the accuracy of the synthesized answers.
Show HN: Aroma: Every TCP Proxy Is Detectable with RTT Fingerprinting
TL;DR explanation (go to <a href="https://github.com/Sakura-sx/Aroma?tab=readme-ov-file#tldr-explanation" rel="nofollow">https://github.com/Sakura-sx/Aroma?tab=readme-ov-file#tldr-e...</a> if you want the formatted version)<p>This is done by measuring the minimum TCP RTT (client.socket.tcpi_min_rtt) seen and the smoothed TCP RTT (client.socket.tcpi_rtt). I am getting this data by using Fastly Custom VCL, they get this data from the Linux kernel (struct tcp_info -> tcpi_min_rtt and tcpi_rtt). I am using Fastly for the Demo since they have PoPs all around the world and they expose TCP socket data to me.<p>The score is calculated by doing tcpi_min_rtt/tcpi_rtt. It's simple but it's what worked best for this with the data Fastly gives me. Based on my testing, 1-0.7 is normal, 0.7-0.3 is normal if the connection is somewhat unstable (WiFi, mobile data, satellite...), 0.3-0.1 is low and may be a proxy, anything lower than 0.1 is flagged as TCP proxy by the current code.
Show HN: Aroma: Every TCP Proxy Is Detectable with RTT Fingerprinting
TL;DR explanation (go to <a href="https://github.com/Sakura-sx/Aroma?tab=readme-ov-file#tldr-explanation" rel="nofollow">https://github.com/Sakura-sx/Aroma?tab=readme-ov-file#tldr-e...</a> if you want the formatted version)<p>This is done by measuring the minimum TCP RTT (client.socket.tcpi_min_rtt) seen and the smoothed TCP RTT (client.socket.tcpi_rtt). I am getting this data by using Fastly Custom VCL, they get this data from the Linux kernel (struct tcp_info -> tcpi_min_rtt and tcpi_rtt). I am using Fastly for the Demo since they have PoPs all around the world and they expose TCP socket data to me.<p>The score is calculated by doing tcpi_min_rtt/tcpi_rtt. It's simple but it's what worked best for this with the data Fastly gives me. Based on my testing, 1-0.7 is normal, 0.7-0.3 is normal if the connection is somewhat unstable (WiFi, mobile data, satellite...), 0.3-0.1 is low and may be a proxy, anything lower than 0.1 is flagged as TCP proxy by the current code.
My app just won best iOS Japanese learning tool of 2025 award (blog)
Show HN: See what readers who loved your favorite book/author also loved to read
Hi HN,<p>Every year, we ask thousands of readers (and authors) to share their 3 favorite reads of the year.<p>Now you can enter a book/author you love and see what books readers loved who also loved that book/author.<p>Try it here:
<a href="https://shepherd.com/bboy/2025" rel="nofollow">https://shepherd.com/bboy/2025</a><p>This goes wide and doesn't try to limit itself to the genre, so you get some interesting results.<p>What do you think?<p>Background:<p>I want better recommendations based on my reading history. I'm incredibly frustrated with what is out there.<p>This system is based on 5,000 readers voting on their 3 favorite reads from 2023 to 2025. So, this covers ~15,000 books and is a high-quality vote. We wanted to keep the dataset small for now while we play with approaches.<p>We are building a full Book DNA app that pulls in your Goodreads history and delivers deeply personalized book recommendations based on people who like similar books (a significant challenge).<p>You can sign up to beta test it here if you want to help me with that:<p><a href="https://docs.google.com/forms/d/1VOm8XOMU0ygMSTSKi9F0nExnGwoRxGV_id80YyIMGVE/viewform?edit_requested=true" rel="nofollow">https://docs.google.com/forms/d/1VOm8XOMU0ygMSTSKi9F0nExnGwo...</a><p>The first beta is coming out in late January, but it's pretty basic to start.<p>Past Show HNs as we've built Shepherd:<p><a href="https://news.ycombinator.com/item?id=40084193">https://news.ycombinator.com/item?id=40084193</a><p><a href="https://news.ycombinator.com/item?id=38600246">https://news.ycombinator.com/item?id=38600246</a><p><a href="https://news.ycombinator.com/item?id=26871660">https://news.ycombinator.com/item?id=26871660</a><p>Thanks, looking forward to your comments :)<p>Ben
Show HN: See what readers who loved your favorite book/author also loved to read
Hi HN,<p>Every year, we ask thousands of readers (and authors) to share their 3 favorite reads of the year.<p>Now you can enter a book/author you love and see what books readers loved who also loved that book/author.<p>Try it here:
<a href="https://shepherd.com/bboy/2025" rel="nofollow">https://shepherd.com/bboy/2025</a><p>This goes wide and doesn't try to limit itself to the genre, so you get some interesting results.<p>What do you think?<p>Background:<p>I want better recommendations based on my reading history. I'm incredibly frustrated with what is out there.<p>This system is based on 5,000 readers voting on their 3 favorite reads from 2023 to 2025. So, this covers ~15,000 books and is a high-quality vote. We wanted to keep the dataset small for now while we play with approaches.<p>We are building a full Book DNA app that pulls in your Goodreads history and delivers deeply personalized book recommendations based on people who like similar books (a significant challenge).<p>You can sign up to beta test it here if you want to help me with that:<p><a href="https://docs.google.com/forms/d/1VOm8XOMU0ygMSTSKi9F0nExnGwoRxGV_id80YyIMGVE/viewform?edit_requested=true" rel="nofollow">https://docs.google.com/forms/d/1VOm8XOMU0ygMSTSKi9F0nExnGwo...</a><p>The first beta is coming out in late January, but it's pretty basic to start.<p>Past Show HNs as we've built Shepherd:<p><a href="https://news.ycombinator.com/item?id=40084193">https://news.ycombinator.com/item?id=40084193</a><p><a href="https://news.ycombinator.com/item?id=38600246">https://news.ycombinator.com/item?id=38600246</a><p><a href="https://news.ycombinator.com/item?id=26871660">https://news.ycombinator.com/item?id=26871660</a><p>Thanks, looking forward to your comments :)<p>Ben
Show HN: See what readers who loved your favorite book/author also loved to read
Hi HN,<p>Every year, we ask thousands of readers (and authors) to share their 3 favorite reads of the year.<p>Now you can enter a book/author you love and see what books readers loved who also loved that book/author.<p>Try it here:
<a href="https://shepherd.com/bboy/2025" rel="nofollow">https://shepherd.com/bboy/2025</a><p>This goes wide and doesn't try to limit itself to the genre, so you get some interesting results.<p>What do you think?<p>Background:<p>I want better recommendations based on my reading history. I'm incredibly frustrated with what is out there.<p>This system is based on 5,000 readers voting on their 3 favorite reads from 2023 to 2025. So, this covers ~15,000 books and is a high-quality vote. We wanted to keep the dataset small for now while we play with approaches.<p>We are building a full Book DNA app that pulls in your Goodreads history and delivers deeply personalized book recommendations based on people who like similar books (a significant challenge).<p>You can sign up to beta test it here if you want to help me with that:<p><a href="https://docs.google.com/forms/d/1VOm8XOMU0ygMSTSKi9F0nExnGwoRxGV_id80YyIMGVE/viewform?edit_requested=true" rel="nofollow">https://docs.google.com/forms/d/1VOm8XOMU0ygMSTSKi9F0nExnGwo...</a><p>The first beta is coming out in late January, but it's pretty basic to start.<p>Past Show HNs as we've built Shepherd:<p><a href="https://news.ycombinator.com/item?id=40084193">https://news.ycombinator.com/item?id=40084193</a><p><a href="https://news.ycombinator.com/item?id=38600246">https://news.ycombinator.com/item?id=38600246</a><p><a href="https://news.ycombinator.com/item?id=26871660">https://news.ycombinator.com/item?id=26871660</a><p>Thanks, looking forward to your comments :)<p>Ben
Show HN: Superset – Terminal to run 10 parallel coding agents
Hey HN, we’re Avi, Kiet, and Satya. We’re building Superset, an open-source terminal made for managing a bunch of coding agents (Claude Code, Codex, etc) in parallel.<p>- Superset makes it easy to spin up git worktrees and automatically setup your environment<p>- Agents and terminal tabs are isolated to worktrees, preventing conflicts<p>- Built-in hooks [0] to notify when your coding agents are done/needs attention,<p>- A diff viewer to review the changes and make PRs quickly<p>We’re three engineers who’ve built and maintained large codebases, and kept wanting to work on as many features in parallel as possible. Git worktrees [1] have been a useful solution for this task but they’re annoying to spin up and manage. We started superset as a tool that uses the best practices we’ve discovered running parallel agents.<p>Here is a demo video:<p><a href="https://www.youtube.com/watch?v=pHJhKFX2S-4" rel="nofollow">https://www.youtube.com/watch?v=pHJhKFX2S-4</a><p>We all use Superset to build Superset, and it more than doubles our productivity (you’ll be able to tell from the autoupdates). We have many friends using it over their IDE of choice or replacing their terminals with Superset, and it seems to stick because they can keep using whatever CLI agent or tool they want while Superset just augments their existing set of tools.<p>Superset is written predominantly in Typescript and based on Electron, xterm.js, and node-pty. We chose xterm+node-pty because it's a proven way to run real PTYs in a desktop app (used by VSCode and Hyper), and Electron lets us ship fast.
Next, we’re exploring features like running worktrees in cloud VMs to offload local resources, context sharing between agents, and a top-level orchestration agent for managing many worktrees or projects at once.<p>We’ve learned a lot building this: making a good terminal is more complex than you’d think, and terminal and git defaults aren’t universal (svn vs git, weird shell setups, complex monorepos, etc.).<p>Building a product for yourself is way faster and quite fun. It's early days, but we’d love you to try Superset across all your CLI tools and environments, we welcome your feedback! :)<p>[0] <a href="https://code.claude.com/docs/en/hooks" rel="nofollow">https://code.claude.com/docs/en/hooks</a><p>[1] <a href="https://git-scm.com/docs/git-worktree" rel="nofollow">https://git-scm.com/docs/git-worktree</a>
Show HN: Superset – Terminal to run 10 parallel coding agents
Hey HN, we’re Avi, Kiet, and Satya. We’re building Superset, an open-source terminal made for managing a bunch of coding agents (Claude Code, Codex, etc) in parallel.<p>- Superset makes it easy to spin up git worktrees and automatically setup your environment<p>- Agents and terminal tabs are isolated to worktrees, preventing conflicts<p>- Built-in hooks [0] to notify when your coding agents are done/needs attention,<p>- A diff viewer to review the changes and make PRs quickly<p>We’re three engineers who’ve built and maintained large codebases, and kept wanting to work on as many features in parallel as possible. Git worktrees [1] have been a useful solution for this task but they’re annoying to spin up and manage. We started superset as a tool that uses the best practices we’ve discovered running parallel agents.<p>Here is a demo video:<p><a href="https://www.youtube.com/watch?v=pHJhKFX2S-4" rel="nofollow">https://www.youtube.com/watch?v=pHJhKFX2S-4</a><p>We all use Superset to build Superset, and it more than doubles our productivity (you’ll be able to tell from the autoupdates). We have many friends using it over their IDE of choice or replacing their terminals with Superset, and it seems to stick because they can keep using whatever CLI agent or tool they want while Superset just augments their existing set of tools.<p>Superset is written predominantly in Typescript and based on Electron, xterm.js, and node-pty. We chose xterm+node-pty because it's a proven way to run real PTYs in a desktop app (used by VSCode and Hyper), and Electron lets us ship fast.
Next, we’re exploring features like running worktrees in cloud VMs to offload local resources, context sharing between agents, and a top-level orchestration agent for managing many worktrees or projects at once.<p>We’ve learned a lot building this: making a good terminal is more complex than you’d think, and terminal and git defaults aren’t universal (svn vs git, weird shell setups, complex monorepos, etc.).<p>Building a product for yourself is way faster and quite fun. It's early days, but we’d love you to try Superset across all your CLI tools and environments, we welcome your feedback! :)<p>[0] <a href="https://code.claude.com/docs/en/hooks" rel="nofollow">https://code.claude.com/docs/en/hooks</a><p>[1] <a href="https://git-scm.com/docs/git-worktree" rel="nofollow">https://git-scm.com/docs/git-worktree</a>
Show HN: Superset – Terminal to run 10 parallel coding agents
Hey HN, we’re Avi, Kiet, and Satya. We’re building Superset, an open-source terminal made for managing a bunch of coding agents (Claude Code, Codex, etc) in parallel.<p>- Superset makes it easy to spin up git worktrees and automatically setup your environment<p>- Agents and terminal tabs are isolated to worktrees, preventing conflicts<p>- Built-in hooks [0] to notify when your coding agents are done/needs attention,<p>- A diff viewer to review the changes and make PRs quickly<p>We’re three engineers who’ve built and maintained large codebases, and kept wanting to work on as many features in parallel as possible. Git worktrees [1] have been a useful solution for this task but they’re annoying to spin up and manage. We started superset as a tool that uses the best practices we’ve discovered running parallel agents.<p>Here is a demo video:<p><a href="https://www.youtube.com/watch?v=pHJhKFX2S-4" rel="nofollow">https://www.youtube.com/watch?v=pHJhKFX2S-4</a><p>We all use Superset to build Superset, and it more than doubles our productivity (you’ll be able to tell from the autoupdates). We have many friends using it over their IDE of choice or replacing their terminals with Superset, and it seems to stick because they can keep using whatever CLI agent or tool they want while Superset just augments their existing set of tools.<p>Superset is written predominantly in Typescript and based on Electron, xterm.js, and node-pty. We chose xterm+node-pty because it's a proven way to run real PTYs in a desktop app (used by VSCode and Hyper), and Electron lets us ship fast.
Next, we’re exploring features like running worktrees in cloud VMs to offload local resources, context sharing between agents, and a top-level orchestration agent for managing many worktrees or projects at once.<p>We’ve learned a lot building this: making a good terminal is more complex than you’d think, and terminal and git defaults aren’t universal (svn vs git, weird shell setups, complex monorepos, etc.).<p>Building a product for yourself is way faster and quite fun. It's early days, but we’d love you to try Superset across all your CLI tools and environments, we welcome your feedback! :)<p>[0] <a href="https://code.claude.com/docs/en/hooks" rel="nofollow">https://code.claude.com/docs/en/hooks</a><p>[1] <a href="https://git-scm.com/docs/git-worktree" rel="nofollow">https://git-scm.com/docs/git-worktree</a>
Show HN: My not-for-profit search engine with no ads, no AI, & all DDG bangs
I've been working on a little open source [1] search engine, nilch. I noticed that nearly all well known search engines, including the alternative ones, tend to be run by companies of various sizes with the goal to make money, so they either fill your results with ads or charge you money, and I dislike this because search is the backbone of the internet and should not be commercial, so it runs in a not-for-profit style and aims to survive on donations. Additionally I'm personally really sick of AI in my search results so I got rid of that, and I wanted DuckDuckGo bangs so it supports all of them. Like many alternative search engines, it is fully private.<p>Sadly, it currently does not have its own index but rather uses the Brave search API. Once I'm in a financial position that it's possible, I would absolutely love to build a completely new index from the ground up which is open source, as well as an open source ranking and search algorithm, to back it.<p>I posted on Reddit and got an amazing amount of feedback which I implemented a number of feature requests, so I would really like your ideas, critiques, and bug reports as well. Thank you and sorry for the long post!<p>[1] <a href="https://github.com/UnmappedStack/nilch" rel="nofollow">https://github.com/UnmappedStack/nilch</a>
Show HN: My not-for-profit search engine with no ads, no AI, & all DDG bangs
I've been working on a little open source [1] search engine, nilch. I noticed that nearly all well known search engines, including the alternative ones, tend to be run by companies of various sizes with the goal to make money, so they either fill your results with ads or charge you money, and I dislike this because search is the backbone of the internet and should not be commercial, so it runs in a not-for-profit style and aims to survive on donations. Additionally I'm personally really sick of AI in my search results so I got rid of that, and I wanted DuckDuckGo bangs so it supports all of them. Like many alternative search engines, it is fully private.<p>Sadly, it currently does not have its own index but rather uses the Brave search API. Once I'm in a financial position that it's possible, I would absolutely love to build a completely new index from the ground up which is open source, as well as an open source ranking and search algorithm, to back it.<p>I posted on Reddit and got an amazing amount of feedback which I implemented a number of feature requests, so I would really like your ideas, critiques, and bug reports as well. Thank you and sorry for the long post!<p>[1] <a href="https://github.com/UnmappedStack/nilch" rel="nofollow">https://github.com/UnmappedStack/nilch</a>
Show HN: Stop Claude Code from forgetting everything
I got tired of Claude Code forgetting all my context every time I open a new session: set-up decisions, how I like my margins, decision history. etc.<p>We built a shared memory layer you can drop in as a Claude Code Skill. It’s basically a tiny memory DB with recall that remembers your sessions. Not magic. Not AGI. Just state.<p>Install in Claude Code:<p><pre><code> /plugin marketplace add https://github.com/mutable-state-inc/ensue-skill
/plugin install ensue-memory
# restart Claude Code
</code></pre>
What it does: (1) persists context between sessions (2) semantic & temportal search (not just string grep). Basically git for your Claude brain<p>What it doesn’t do: - it won’t read your mind - it’s alpha; it might break if you throw a couch at it<p>Repo: <a href="https://github.com/mutable-state-inc/ensue-skill" rel="nofollow">https://github.com/mutable-state-inc/ensue-skill</a><p>If you try it and it sucks, tell me why so I can fix it. Don't be kind, tia
Show HN: Stop Claude Code from forgetting everything
I got tired of Claude Code forgetting all my context every time I open a new session: set-up decisions, how I like my margins, decision history. etc.<p>We built a shared memory layer you can drop in as a Claude Code Skill. It’s basically a tiny memory DB with recall that remembers your sessions. Not magic. Not AGI. Just state.<p>Install in Claude Code:<p><pre><code> /plugin marketplace add https://github.com/mutable-state-inc/ensue-skill
/plugin install ensue-memory
# restart Claude Code
</code></pre>
What it does: (1) persists context between sessions (2) semantic & temportal search (not just string grep). Basically git for your Claude brain<p>What it doesn’t do: - it won’t read your mind - it’s alpha; it might break if you throw a couch at it<p>Repo: <a href="https://github.com/mutable-state-inc/ensue-skill" rel="nofollow">https://github.com/mutable-state-inc/ensue-skill</a><p>If you try it and it sucks, tell me why so I can fix it. Don't be kind, tia
Show HN: Stop Claude Code from forgetting everything
I got tired of Claude Code forgetting all my context every time I open a new session: set-up decisions, how I like my margins, decision history. etc.<p>We built a shared memory layer you can drop in as a Claude Code Skill. It’s basically a tiny memory DB with recall that remembers your sessions. Not magic. Not AGI. Just state.<p>Install in Claude Code:<p><pre><code> /plugin marketplace add https://github.com/mutable-state-inc/ensue-skill
/plugin install ensue-memory
# restart Claude Code
</code></pre>
What it does: (1) persists context between sessions (2) semantic & temportal search (not just string grep). Basically git for your Claude brain<p>What it doesn’t do: - it won’t read your mind - it’s alpha; it might break if you throw a couch at it<p>Repo: <a href="https://github.com/mutable-state-inc/ensue-skill" rel="nofollow">https://github.com/mutable-state-inc/ensue-skill</a><p>If you try it and it sucks, tell me why so I can fix it. Don't be kind, tia
Show HN: Vibe coding a bookshelf with Claude Code
Show HN: Vibe coding a bookshelf with Claude Code
Show HN: Z80-μLM, a 'Conversational AI' That Fits in 40KB
How small can a language model be while still doing something useful? I wanted to find out, and had some spare time over the holidays.<p>Z80-μLM is a character-level language model with 2-bit quantized weights ({-2,-1,0,+1}) that runs on a Z80 with 64KB RAM. The entire thing: inference, weights, chat UI, it all fits in a 40KB .COM file that you can run in a CP/M emulator and hopefully even real hardware!<p>It won't write your emails, but it can be trained to play a stripped down version of 20 Questions, and is sometimes able to maintain the illusion of having simple but terse conversations with a distinct personality.<p>--<p>The extreme constraints nerd-sniped me and forced interesting trade-offs: trigram hashing (typo-tolerant, loses word order), 16-bit integer math, and some careful massaging of the training data meant I could keep the examples 'interesting'.<p>The key was quantization-aware training that accurately models the inference code limitations. The training loop runs both float and integer-quantized forward passes in parallel, scoring the model on how well its knowledge survives quantization. The weights are progressively pushed toward the 2-bit grid using straight-through estimators, with overflow penalties matching the Z80's 16-bit accumulator limits. By the end of training, the model has already adapted to its constraints, so no post-hoc quantization collapse.<p>Eventually I ended up spending a few dollars on Claude API to generate 20 questions data (see examples/guess/GUESS.COM), I hope Anthropic won't send me a C&D for distilling their model against the ToS ;P<p>But anyway, happy code-golf season everybody :)
Show HN: Z80-μLM, a 'Conversational AI' That Fits in 40KB
How small can a language model be while still doing something useful? I wanted to find out, and had some spare time over the holidays.<p>Z80-μLM is a character-level language model with 2-bit quantized weights ({-2,-1,0,+1}) that runs on a Z80 with 64KB RAM. The entire thing: inference, weights, chat UI, it all fits in a 40KB .COM file that you can run in a CP/M emulator and hopefully even real hardware!<p>It won't write your emails, but it can be trained to play a stripped down version of 20 Questions, and is sometimes able to maintain the illusion of having simple but terse conversations with a distinct personality.<p>--<p>The extreme constraints nerd-sniped me and forced interesting trade-offs: trigram hashing (typo-tolerant, loses word order), 16-bit integer math, and some careful massaging of the training data meant I could keep the examples 'interesting'.<p>The key was quantization-aware training that accurately models the inference code limitations. The training loop runs both float and integer-quantized forward passes in parallel, scoring the model on how well its knowledge survives quantization. The weights are progressively pushed toward the 2-bit grid using straight-through estimators, with overflow penalties matching the Z80's 16-bit accumulator limits. By the end of training, the model has already adapted to its constraints, so no post-hoc quantization collapse.<p>Eventually I ended up spending a few dollars on Claude API to generate 20 questions data (see examples/guess/GUESS.COM), I hope Anthropic won't send me a C&D for distilling their model against the ToS ;P<p>But anyway, happy code-golf season everybody :)
Show HN: Pion SCTP with RACK is 70% faster with 30% less latency
SCTP is a low level protocol focused on reliable packet transmission. Unlike hopelessly flinging packets from one device to another, it makes sure that the packets are correct using CRC, removes duplicate packets, and allows for packets to be sent in any order.<p>Going into an established library, I thought that everything was already implemented and that there wasn't anything to do until I went through the existing issues and organized all the tasks and decided on an order. Sean DuBois (<a href="https://github.com/Sean-Der" rel="nofollow">https://github.com/Sean-Der</a>), one of the co-creators and current maintainers of Pion, an open-source pure Go implementation of WebRTC (which uses SCTP), introduced me to a dissertation that was written about improving SCTP from 2021 (<a href="https://duepublico2.uni-due.de/servlets/MCRFileNodeServlet/duepublico_derivate_00073893/Diss_Weinrank.pdf" rel="nofollow">https://duepublico2.uni-due.de/servlets/MCRFileNodeServlet/d...</a>). To my surprise, the features in it weren't actually implemented yet, and generally went unused even though it depicted pretty big improvements. This came as a bit of a shock to me considering the countless companies and services that actively use Pion with millions of users on a daily basis.<p>This led to two things: 1) implement the feature (done by me) and 2) measure the performance (done by Joe Turki <a href="https://github.com/JoeTurki" rel="nofollow">https://github.com/JoeTurki</a>). If you're interested in reading more, please check out the blog post where we go over what SCTP is used for, how I improved it, and the effort that went into making such a large improvement possible.<p>This also marks a huge milestone for other companies and services that use SCTP as they can refer to the implementation in Pion for their own SCTP libraries including any real-time streaming platforms such as Microsoft Teams, Discord screen share, Twitch guest star, and many more!<p>For my personal background, please take a look at a comment below about what it was like for me to get started with open-source and my career related journeys. Thanks for reading!