Tuesday, July 28, 2026
Monday, July 27, 2026
Sunday, July 26, 2026
Saturday, July 25, 2026
New top story on Hacker News: Show HN: I made some transistor animations
Show HN: I made some transistor animations
27 by stunningllama | 2 comments on Hacker News.
Hi HN, I made some animations of the most important kinds of transistors using my semiconductor simulation, details of which are on the page. I tried to make the visuals as realistic as possible while also aiming for clarity. If you want to go beyond the charge carriers and look at, for example, the electric field, you can do so in the simulation software. The desktop software also has less common devices like IBGTs and SCRs that have similar animations. The last thread about my software was posted here about a year ago: https://ift.tt/ayt2hKO
27 by stunningllama | 2 comments on Hacker News.
Hi HN, I made some animations of the most important kinds of transistors using my semiconductor simulation, details of which are on the page. I tried to make the visuals as realistic as possible while also aiming for clarity. If you want to go beyond the charge carriers and look at, for example, the electric field, you can do so in the simulation software. The desktop software also has less common devices like IBGTs and SCRs that have similar animations. The last thread about my software was posted here about a year ago: https://ift.tt/ayt2hKO
Friday, July 24, 2026
Thursday, July 23, 2026
New top story on Hacker News: Show HN: Trifle – Open-source analytics that stores answers, not events
Show HN: Trifle – Open-source analytics that stores answers, not events
7 by iluzone | 0 comments on Hacker News.
Trifle is an open-source time-series analytics library that aggregates nested counters instead of storing raw events. All in the database you already have. After rebuilding it twice over 10 years, it now tracks ~1B events a day at my day job. It started in 2015 as my own Rails APM. I plugged into ActiveSupport::Notifications, got a few small users, and one bigger one whose scraping app broke everything. That sparked the core idea: aggregate counters into pre-defined time buckets, so a single write increments multiple buckets at once. The APM eventually faded away without much traction. Later in 2021 I needed analytics at my day job. Instead of going for something out there I revised the idea of Trifle as a more generic analytics library, borrowing some data warehouse ideas. First used Redis, then Postgres, eventually MongoDB. Hence why Trifle::Stats comes with multiple drivers that keep the DSL unified while storage layer changes with your needs. In our case (huge write volume, some reads) PG read faster but slowed on large writes. The nested values are the whole trick here. Single: Trifle::Stats.track( key: 'requests::aws::s3_uploads', values: { count: 1, status: { request.response_code => 1 }, size: payload.bytes, duration: { sum: request.duration, count: 1 } } ) builds up counts for requests, success rate, result status codes, duration for multiple time buckets at once. Single bucket from 2am then looks like: { count: 14, status: { 200: 12, 500: 2 }, size: 5628341, duration: { sum: 43, count: 14 } } If request.duration is in seconds, then sum stored under duration would be in seconds as well. Success rate is never stored, but it is calculated by dividing 200s over total number of requests. Same with average duration: sum over count. You ask for a metrics key, granularity and timeframe and you get back aggregated values at each point. Ready for charts or to answer "Average response time over last 30 days". There's a Series wrapper for aggregating and formatting values for charts in a simple call. And as building dashboards is not as much fun for other devs as I thought, I built Trifle App - a visual layer with dashboards, scheduled digests and alerts. It's written in Elixir, so I ported the library to Elixir too. And later to Go for a CLI. All three are compatible, write in one and read in another. Today we track activity from over 100M background jobs a day which turns into about 1B events. It runs surprisingly cheap when you're willing to trade some safety away (turn off journaling and write concerns in Mongo). 3-node Hetzner MongoDB cluster where the primary does 20% utilization costs us around $1k/month. It has its limitations. Payloads can't hold tens of thousands of keys. Documents becomes too large to update efficiently. Some planning ahead is needed. And then there are no dimensions. Sometimes you can nest them (country - there are only so many countries), sometimes it's better to have dedicated metrics key per dimension (customer - growing forever). That multiplies tracked events, hence 1B events from 100M jobs. The libraries are MIT. The App is source-available under ELv2 - free to self-host and paid cloud if you want it managed. I build this on the side with no investor money to burn on a free service. Happy to answer anything about architecture, storage models, my failures or why I didn't give up on this yet.
7 by iluzone | 0 comments on Hacker News.
Trifle is an open-source time-series analytics library that aggregates nested counters instead of storing raw events. All in the database you already have. After rebuilding it twice over 10 years, it now tracks ~1B events a day at my day job. It started in 2015 as my own Rails APM. I plugged into ActiveSupport::Notifications, got a few small users, and one bigger one whose scraping app broke everything. That sparked the core idea: aggregate counters into pre-defined time buckets, so a single write increments multiple buckets at once. The APM eventually faded away without much traction. Later in 2021 I needed analytics at my day job. Instead of going for something out there I revised the idea of Trifle as a more generic analytics library, borrowing some data warehouse ideas. First used Redis, then Postgres, eventually MongoDB. Hence why Trifle::Stats comes with multiple drivers that keep the DSL unified while storage layer changes with your needs. In our case (huge write volume, some reads) PG read faster but slowed on large writes. The nested values are the whole trick here. Single: Trifle::Stats.track( key: 'requests::aws::s3_uploads', values: { count: 1, status: { request.response_code => 1 }, size: payload.bytes, duration: { sum: request.duration, count: 1 } } ) builds up counts for requests, success rate, result status codes, duration for multiple time buckets at once. Single bucket from 2am then looks like: { count: 14, status: { 200: 12, 500: 2 }, size: 5628341, duration: { sum: 43, count: 14 } } If request.duration is in seconds, then sum stored under duration would be in seconds as well. Success rate is never stored, but it is calculated by dividing 200s over total number of requests. Same with average duration: sum over count. You ask for a metrics key, granularity and timeframe and you get back aggregated values at each point. Ready for charts or to answer "Average response time over last 30 days". There's a Series wrapper for aggregating and formatting values for charts in a simple call. And as building dashboards is not as much fun for other devs as I thought, I built Trifle App - a visual layer with dashboards, scheduled digests and alerts. It's written in Elixir, so I ported the library to Elixir too. And later to Go for a CLI. All three are compatible, write in one and read in another. Today we track activity from over 100M background jobs a day which turns into about 1B events. It runs surprisingly cheap when you're willing to trade some safety away (turn off journaling and write concerns in Mongo). 3-node Hetzner MongoDB cluster where the primary does 20% utilization costs us around $1k/month. It has its limitations. Payloads can't hold tens of thousands of keys. Documents becomes too large to update efficiently. Some planning ahead is needed. And then there are no dimensions. Sometimes you can nest them (country - there are only so many countries), sometimes it's better to have dedicated metrics key per dimension (customer - growing forever). That multiplies tracked events, hence 1B events from 100M jobs. The libraries are MIT. The App is source-available under ELv2 - free to self-host and paid cloud if you want it managed. I build this on the side with no investor money to burn on a free service. Happy to answer anything about architecture, storage models, my failures or why I didn't give up on this yet.
Wednesday, July 22, 2026
New top story on Hacker News: ICE shared Medicaid data it wasn't supposed to have with Palantir
ICE shared Medicaid data it wasn't supposed to have with Palantir
30 by Jimmc414 | 2 comments on Hacker News.
30 by Jimmc414 | 2 comments on Hacker News.
Tuesday, July 21, 2026
Monday, July 20, 2026
Sunday, July 19, 2026
Saturday, July 18, 2026
Friday, July 17, 2026
Thursday, July 16, 2026
Wednesday, July 15, 2026
Tuesday, July 14, 2026
Monday, July 13, 2026
Sunday, July 12, 2026
Saturday, July 11, 2026
Friday, July 10, 2026
Thursday, July 9, 2026
New top story on Hacker News: Show HN: Abralo – Free, easy way to run several Claude Code agents in one window
Show HN: Abralo – Free, easy way to run several Claude Code agents in one window
15 by cwbuilds | 4 comments on Hacker News.
Hi guys, I've been using Claude Code for almost everything lately. Have given one an email account so it can research business leads, draft emails, fact-check them and clear them with me before sending (works really well by the way). I also tend to have a few Claude Code agents running at any one time for coding. I used to create a split terminal to manage them from there, but found working in the terminal all day pretty depressing and, more importantly, found it hard to follow Claude Code's process and see which agents needed my immediate attention. I tried Anthropic's VS Code Claude Code extension and it had a great UI (more info on Claude Code's process and easier to read), but it crashed my PC when I ran more than 3 and I couldn't watch multiple agents in parallel (had to constantly switch between them). So I built a lightweight Tauri desktop app which lets you run multiple Claude Code agents in one window alongside each other. It's easier to read the output and see which agents need your attention than a terminal. Have been using this all day everyday instead of an IDE and have obsessed over every detail to make sure it's easy-to-use, but also lightweight and fast (so you can manage multiple agents without your PC crashing). There are some nice features like better usage alerts for when you're going to hit your 5-hour and weekly limits (with sparklines to show when usage peaked, and which agents are the most token-intensive). It's free to use (you just need to log in with your existing Claude Code account) for up to 4 agents simultaneously. This app doesn't store your Claude Code account details and doesn't store any of your interactions with Claude Code. They remain between you and Anthropic. It's compatible with Windows, MacOS and 64-bit Linux. Would really appreciate any feedback, so if you have any thoughts, issues or suggestions please let me know. Thanks, Chris
15 by cwbuilds | 4 comments on Hacker News.
Hi guys, I've been using Claude Code for almost everything lately. Have given one an email account so it can research business leads, draft emails, fact-check them and clear them with me before sending (works really well by the way). I also tend to have a few Claude Code agents running at any one time for coding. I used to create a split terminal to manage them from there, but found working in the terminal all day pretty depressing and, more importantly, found it hard to follow Claude Code's process and see which agents needed my immediate attention. I tried Anthropic's VS Code Claude Code extension and it had a great UI (more info on Claude Code's process and easier to read), but it crashed my PC when I ran more than 3 and I couldn't watch multiple agents in parallel (had to constantly switch between them). So I built a lightweight Tauri desktop app which lets you run multiple Claude Code agents in one window alongside each other. It's easier to read the output and see which agents need your attention than a terminal. Have been using this all day everyday instead of an IDE and have obsessed over every detail to make sure it's easy-to-use, but also lightweight and fast (so you can manage multiple agents without your PC crashing). There are some nice features like better usage alerts for when you're going to hit your 5-hour and weekly limits (with sparklines to show when usage peaked, and which agents are the most token-intensive). It's free to use (you just need to log in with your existing Claude Code account) for up to 4 agents simultaneously. This app doesn't store your Claude Code account details and doesn't store any of your interactions with Claude Code. They remain between you and Anthropic. It's compatible with Windows, MacOS and 64-bit Linux. Would really appreciate any feedback, so if you have any thoughts, issues or suggestions please let me know. Thanks, Chris
Wednesday, July 8, 2026
Tuesday, July 7, 2026
New top story on Hacker News: Show HN: Halo – open-source, tamper-evident runtime evidence for AI agents
Show HN: Halo – open-source, tamper-evident runtime evidence for AI agents
3 by brian_kuan | 0 comments on Hacker News.
Hi HN, I'm Brian, I spent the last few years at Vanta (YC W18), helping startups and enterprises become compliant and I recently started exploring what that might look like in a post-agentic world. The problem Halo solves is: when a company buys an AI agent from a vendor and gives it access to their data, they have no way to check what the agent did with that data. Vendors may have built observability dashboards and audit logs, but those are editable and partisan. SOC 2 and ISO 27001 audit a company's controls, but controls are less predictive when the software is agentic. TLDR: give an agent the same prompt 50 times, and you get 50 slightly different actions/answers - so the only thing worth auditing in a post-agentic world is what happened at runtime. Halo is an open-source project that produces agent runtime evidence. It's a small recorder that records every action an agent takes (eg. tool calls, model calls, data access, etc), and becomes a record in an append-only log. It's hash-chained, so anyone can re-verify. Run the following command to see a fictional example: uvx --from halo-record halo demo --serve Then, delete a line from one of the .jsonl files and reload, and the report will catch that it's been tampered with. To wire up your own agent, run this line of Python: agent = trace(run_my_agent, profile="my-agent", log="audit.jsonl") Then use this to generate a real report and give it to your customers: halo report audit.jsonl -o report.html Disclaimer: this proves integrity, not completeness (as a self-held chain proves nothing was edited but does NOT prove that nothing was omitted). Catching this requires a witness outside the vendor and is what I'm working on next. Halo is Apache-2.0, contains zero runtime dependencies, and is about 4,300 lines of Python with 125 tests (if you prefer TypeScript, here's that repo: https://ift.tt/ga7Af5w ). Give it a try, and please let me know if you have any feedback!
3 by brian_kuan | 0 comments on Hacker News.
Hi HN, I'm Brian, I spent the last few years at Vanta (YC W18), helping startups and enterprises become compliant and I recently started exploring what that might look like in a post-agentic world. The problem Halo solves is: when a company buys an AI agent from a vendor and gives it access to their data, they have no way to check what the agent did with that data. Vendors may have built observability dashboards and audit logs, but those are editable and partisan. SOC 2 and ISO 27001 audit a company's controls, but controls are less predictive when the software is agentic. TLDR: give an agent the same prompt 50 times, and you get 50 slightly different actions/answers - so the only thing worth auditing in a post-agentic world is what happened at runtime. Halo is an open-source project that produces agent runtime evidence. It's a small recorder that records every action an agent takes (eg. tool calls, model calls, data access, etc), and becomes a record in an append-only log. It's hash-chained, so anyone can re-verify. Run the following command to see a fictional example: uvx --from halo-record halo demo --serve Then, delete a line from one of the .jsonl files and reload, and the report will catch that it's been tampered with. To wire up your own agent, run this line of Python: agent = trace(run_my_agent, profile="my-agent", log="audit.jsonl") Then use this to generate a real report and give it to your customers: halo report audit.jsonl -o report.html Disclaimer: this proves integrity, not completeness (as a self-held chain proves nothing was edited but does NOT prove that nothing was omitted). Catching this requires a witness outside the vendor and is what I'm working on next. Halo is Apache-2.0, contains zero runtime dependencies, and is about 4,300 lines of Python with 125 tests (if you prefer TypeScript, here's that repo: https://ift.tt/ga7Af5w ). Give it a try, and please let me know if you have any feedback!