We Are the Last People Who Know How It Works
77 by cylo | 28 comments on Hacker News.
Tuesday, 30 June 2026
Monday, 29 June 2026
Sunday, 28 June 2026
New top story on Hacker News: I used Claude Code to get a second opinion on my MRI
I used Claude Code to get a second opinion on my MRI
106 by engmarketer | 144 comments on Hacker News.
106 by engmarketer | 144 comments on Hacker News.
Saturday, 27 June 2026
Friday, 26 June 2026
New top story on Hacker News: Ask HN: Is "no source code was copied" still a sufficient copyright defense?
Ask HN: Is "no source code was copied" still a sufficient copyright defense?
17 by oscgam1 | 13 comments on Hacker News.
We are all familiar with the Corgi event: https://ift.tt/HtAY4cl With the barrier to create new apps having dropped significantly thanks to LLMs, I am seeing more cases about copyright and unfair competition. I've seen and participated in some of these cases. Usually expert witnesses are required. Curious to hear the community stance on this one. "Now software developers are feeling what authors and artist felt". https://ift.tt/PhfCA9Q There are several claims of: Copying UI is Ok, your product is not undifferentiated enough. Here is a legal assessment of the situation: https://ift.tt/SFn8Hhp
17 by oscgam1 | 13 comments on Hacker News.
We are all familiar with the Corgi event: https://ift.tt/HtAY4cl With the barrier to create new apps having dropped significantly thanks to LLMs, I am seeing more cases about copyright and unfair competition. I've seen and participated in some of these cases. Usually expert witnesses are required. Curious to hear the community stance on this one. "Now software developers are feeling what authors and artist felt". https://ift.tt/PhfCA9Q There are several claims of: Copying UI is Ok, your product is not undifferentiated enough. Here is a legal assessment of the situation: https://ift.tt/SFn8Hhp
Thursday, 25 June 2026
New top story on Hacker News: Show HN: OpenKnowledge – open source AI-first alternative to Obsidian/Notion
Show HN: OpenKnowledge – open source AI-first alternative to Obsidian/Notion
27 by engomez | 5 comments on Hacker News.
Hi HN, Nick here. We’re launching OpenKnowledge ( https://ift.tt/JUP5NFR ), a “what you see is what you get” markdown editor that has direct integrations with Claude, Codex, and Cursor. Available as MacOS app or CLI. Fully free/local and OSS ( https://ift.tt/EX0F1sr ). We built this because we wanted a “Google docs” like experience for writing and sharing markdown files across our team. Obsidian is the best alternative we tried, but found it doesn’t have a true “what you see is what you get” UI and it didn’t integrate well with Claude/Codex outside of community plugins. So we built OpenKnowledge. It takes shape as: 1. A MacOS app with a file navigator, the WYSIWYG editor, and link explorer. 2. Integrations with the Claude, Codex, and Cursor desktop apps. The agents can open an OpenKnowledge editor within their embedded web browsers for a side-by-side experience. 3. Built-in mcps, skills, and RAG for LLM-wiki and “AI Second Brain” scenarios + spec writing 4. An embedded terminal and CLI for TUI-first users OSS stack includes: Tiptap/prosemirror, CodeMirror, yjs (CRDT), Electron (MacOS app), Orama, remark/rehype/micromark/mdast, @pierre/trees On the architecture side, the interesting eng. challenges included: 1. A pipeline to convert ProseMirror to markdown in a bidirectional lossless way. ProseMirror uses ASTs, which are not designed to have byte-fidelity. 2. A dual-observer CRDT to keep the ProseMirror and markdown state in-sync. The CRDT + git also power a collaborative experience that shows what Agents are doing in the markdown, have undo/redo, and version history. The “Share” and cloud-sync functionality are geared for team collaboration. They feel “no-code” but leverage git/GitHub under the hood, which also means data stays fully private. In that spirit, we made OpenKnowledge open source for anybody who’s curious or who’d like to contribute. We’re actively thinking about plugins/extensibility and what’s next. If you have suggestions or feedback, would love to hear it.
27 by engomez | 5 comments on Hacker News.
Hi HN, Nick here. We’re launching OpenKnowledge ( https://ift.tt/JUP5NFR ), a “what you see is what you get” markdown editor that has direct integrations with Claude, Codex, and Cursor. Available as MacOS app or CLI. Fully free/local and OSS ( https://ift.tt/EX0F1sr ). We built this because we wanted a “Google docs” like experience for writing and sharing markdown files across our team. Obsidian is the best alternative we tried, but found it doesn’t have a true “what you see is what you get” UI and it didn’t integrate well with Claude/Codex outside of community plugins. So we built OpenKnowledge. It takes shape as: 1. A MacOS app with a file navigator, the WYSIWYG editor, and link explorer. 2. Integrations with the Claude, Codex, and Cursor desktop apps. The agents can open an OpenKnowledge editor within their embedded web browsers for a side-by-side experience. 3. Built-in mcps, skills, and RAG for LLM-wiki and “AI Second Brain” scenarios + spec writing 4. An embedded terminal and CLI for TUI-first users OSS stack includes: Tiptap/prosemirror, CodeMirror, yjs (CRDT), Electron (MacOS app), Orama, remark/rehype/micromark/mdast, @pierre/trees On the architecture side, the interesting eng. challenges included: 1. A pipeline to convert ProseMirror to markdown in a bidirectional lossless way. ProseMirror uses ASTs, which are not designed to have byte-fidelity. 2. A dual-observer CRDT to keep the ProseMirror and markdown state in-sync. The CRDT + git also power a collaborative experience that shows what Agents are doing in the markdown, have undo/redo, and version history. The “Share” and cloud-sync functionality are geared for team collaboration. They feel “no-code” but leverage git/GitHub under the hood, which also means data stays fully private. In that spirit, we made OpenKnowledge open source for anybody who’s curious or who’d like to contribute. We’re actively thinking about plugins/extensibility and what’s next. If you have suggestions or feedback, would love to hear it.
Wednesday, 24 June 2026
New top story on Hacker News: NSA lost access to Mythos amid Anthropic dispute
NSA lost access to Mythos amid Anthropic dispute
53 by thm | 16 comments on Hacker News.
Unlocked: https://ift.tt/Fw72R0T...
53 by thm | 16 comments on Hacker News.
Unlocked: https://ift.tt/Fw72R0T...
Tuesday, 23 June 2026
Monday, 22 June 2026
New top story on Hacker News: Bain tests software takeover targets by vibecoding AI replicas
Bain tests software takeover targets by vibecoding AI replicas
9 by macleginn | 18 comments on Hacker News.
https://ift.tt/s1ul3g0
9 by macleginn | 18 comments on Hacker News.
https://ift.tt/s1ul3g0
Sunday, 21 June 2026
New top story on Hacker News: Show HN: Pulse – Dashboard for Claude Code, approve tool calls from your phone
Show HN: Pulse – Dashboard for Claude Code, approve tool calls from your phone
9 by nikitadvd | 3 comments on Hacker News.
Hi everyone, I'm a student from Flanders and I like to use Claude Code for my purposes, ideas and also just for fun and also make solutions for problems in our world!) So that's why I built "Pulse", it's an local application that you can easily install to your device and easily follow what your claude agent is doing right now in your terminal session with an ambiance design and easily give permissions for your agent. For those who wants to see directly how much tokens you spent, and how much the session costs, and approve tool calls from everywhere from your phone and everything runs locally without an account can install Pulse from GitHub: https://ift.tt/qR46uQz
9 by nikitadvd | 3 comments on Hacker News.
Hi everyone, I'm a student from Flanders and I like to use Claude Code for my purposes, ideas and also just for fun and also make solutions for problems in our world!) So that's why I built "Pulse", it's an local application that you can easily install to your device and easily follow what your claude agent is doing right now in your terminal session with an ambiance design and easily give permissions for your agent. For those who wants to see directly how much tokens you spent, and how much the session costs, and approve tool calls from everywhere from your phone and everything runs locally without an account can install Pulse from GitHub: https://ift.tt/qR46uQz
Saturday, 20 June 2026
Friday, 19 June 2026
Thursday, 18 June 2026
New top story on Hacker News: Agentic Resource Discovery Specification
Agentic Resource Discovery Specification
6 by damick | 0 comments on Hacker News.
https://ift.tt/Asnqhry...
6 by damick | 0 comments on Hacker News.
https://ift.tt/Asnqhry...
Wednesday, 17 June 2026
New top story on Hacker News: The hacker sent by Anthropic to calm the government's nerves about AI safety
The hacker sent by Anthropic to calm the government's nerves about AI safety
3 by Brajeshwar | 0 comments on Hacker News.
https://ift.tt/hPUbQJc...
3 by Brajeshwar | 0 comments on Hacker News.
https://ift.tt/hPUbQJc...
Tuesday, 16 June 2026
Monday, 15 June 2026
Sunday, 14 June 2026
New top story on Hacker News: Show HN: Trace – Offline Mac meeting transcripts you can flag mid-call
Show HN: Trace – Offline Mac meeting transcripts you can flag mid-call
11 by AG342 | 2 comments on Hacker News.
I'm the developer of Trace, a non-intrusive, shortcut-driven Mac app that records and transcribes your meetings on-device. I know, another meeting transcription app. Please bear with me though, I'm confident that this is at least a little novel. I primarily built Trace for myself. I'd been using MacWhisper, but there was enough fiddling before each call that I'd forget to start it and walk out of an hour-long meeting with nothing written down. So the things I cared about most were that it's quick to activate and stays out of the way. You activate Trace by pressing a global shortcut (configurable), which reveals a small bar at the bottom of your screen (there's also a keystroke and/or option to hide it entirely if you'd rather not see it at all). As I was building it I wanted to bake in a couple of workflows I'd wished for in other transcription apps. 1. Mid-meeting you can press another global shortcut to mark a "key moment" and type a note. The note shows up in the resulting transcript inline at that timestamp. I wanted to add this because I kept catching myself thinking "wait, that bit matters" in meetings and reaching to jot it down in a separate app like Obsidian, which I then needed to add context to, which took me out of the meeting. I use it all the time. If I paste the transcript into an LLM afterwards (which I find myself doing more and more these days) the important moments are flagged so it doesn't gloss over them. This is more noticeable in longer meetings with lots of topics. 2. With another keyboard shortcut you can summon a rough live recap (subtitles, basically) to quickly recap what's just been said. Trace uses standard macOS microphone and system recording APIs to capture both sides of the conversation as two separate tracks and then runs the system side through on-device diarization to identify speakers. Right now we only label them as "Speaker 1", "Speaker 2", etc but there are plans for speaker labelling in the future. You can also show a "live recap" as the call is happening to review what someone just said. All transcription models run on your machine. To be clear though, Trace doesn't do any of the summarising itself, it just produces a markdown transcript, so if you want summaries then you need to pass the output to an AI. The app is sandboxed and your audio/transcripts are never uploaded anywhere - they just exist as audio files and markdown on disk. The only network call Trace is required to make is on the first run to download the speech and speaker models (around 500MB) from Hugging Face, and after that it can be used fully offline. If enabled, a Google Calendar integration can auto-name sessions but that needs a network connection. The app is £9.99 on the macOS App Store. I've been using it every day for months now and I'm super happy with how it's improved my workflow. Feedback very welcome.
11 by AG342 | 2 comments on Hacker News.
I'm the developer of Trace, a non-intrusive, shortcut-driven Mac app that records and transcribes your meetings on-device. I know, another meeting transcription app. Please bear with me though, I'm confident that this is at least a little novel. I primarily built Trace for myself. I'd been using MacWhisper, but there was enough fiddling before each call that I'd forget to start it and walk out of an hour-long meeting with nothing written down. So the things I cared about most were that it's quick to activate and stays out of the way. You activate Trace by pressing a global shortcut (configurable), which reveals a small bar at the bottom of your screen (there's also a keystroke and/or option to hide it entirely if you'd rather not see it at all). As I was building it I wanted to bake in a couple of workflows I'd wished for in other transcription apps. 1. Mid-meeting you can press another global shortcut to mark a "key moment" and type a note. The note shows up in the resulting transcript inline at that timestamp. I wanted to add this because I kept catching myself thinking "wait, that bit matters" in meetings and reaching to jot it down in a separate app like Obsidian, which I then needed to add context to, which took me out of the meeting. I use it all the time. If I paste the transcript into an LLM afterwards (which I find myself doing more and more these days) the important moments are flagged so it doesn't gloss over them. This is more noticeable in longer meetings with lots of topics. 2. With another keyboard shortcut you can summon a rough live recap (subtitles, basically) to quickly recap what's just been said. Trace uses standard macOS microphone and system recording APIs to capture both sides of the conversation as two separate tracks and then runs the system side through on-device diarization to identify speakers. Right now we only label them as "Speaker 1", "Speaker 2", etc but there are plans for speaker labelling in the future. You can also show a "live recap" as the call is happening to review what someone just said. All transcription models run on your machine. To be clear though, Trace doesn't do any of the summarising itself, it just produces a markdown transcript, so if you want summaries then you need to pass the output to an AI. The app is sandboxed and your audio/transcripts are never uploaded anywhere - they just exist as audio files and markdown on disk. The only network call Trace is required to make is on the first run to download the speech and speaker models (around 500MB) from Hugging Face, and after that it can be used fully offline. If enabled, a Google Calendar integration can auto-name sessions but that needs a network connection. The app is £9.99 on the macOS App Store. I've been using it every day for months now and I'm super happy with how it's improved my workflow. Feedback very welcome.
Saturday, 13 June 2026
Friday, 12 June 2026
Thursday, 11 June 2026
New top story on Hacker News: Show HN: I built a Red Flag Warning zone-check tool for the East Bay in 48h
Show HN: I built a Red Flag Warning zone-check tool for the East Bay in 48h
8 by vedant28t | 0 comments on Hacker News.
Hey HN. I'm a high schooler in Fremont, CA. Tuesday morning I got a county-wide AC Alert text telling everyone in Alameda County to prepare a go-bag for an East Bay Hills Red Flag Warning that starts tonight at 11 PM. The text went to ~half a million phones. The actual NWS warning polygon only covers East Bay Hills (NWS zone CAZ515). Most people who got the text don't need a go-bag tonight. Some in the hills don't realize how close they are. So I built this tool - https://ift.tt/oDO5Pi7 mit licensed public github - https://ift.tt/7bic3k9 It does a few things - tells people if they are in the flagged zone, and also provides a way to check if a buddy is in flagged zone and send them a text. Everything without installing an app. I heard back from Oakland Firesafe Council director about a gap in my understanding (and the tool). To my surprise, and through feedback, I realized that you cannot assume that only the flagged area is at risk. Adjacent areas are at risk too! Fires do not follow zone boundaries! I fixed the tool. I built this in 48 hours to close that specific gap: type your address, get a yes/no on whether the NWS polygon covers it, your Genasys evacuation zone, tonight's wind + humidity at your point, a plain-English action checklist, a per-school decision view for East Bay districts, and a one-tap iMessage buddy-check template for a hill-neighbor at 10:30 PM.
8 by vedant28t | 0 comments on Hacker News.
Hey HN. I'm a high schooler in Fremont, CA. Tuesday morning I got a county-wide AC Alert text telling everyone in Alameda County to prepare a go-bag for an East Bay Hills Red Flag Warning that starts tonight at 11 PM. The text went to ~half a million phones. The actual NWS warning polygon only covers East Bay Hills (NWS zone CAZ515). Most people who got the text don't need a go-bag tonight. Some in the hills don't realize how close they are. So I built this tool - https://ift.tt/oDO5Pi7 mit licensed public github - https://ift.tt/7bic3k9 It does a few things - tells people if they are in the flagged zone, and also provides a way to check if a buddy is in flagged zone and send them a text. Everything without installing an app. I heard back from Oakland Firesafe Council director about a gap in my understanding (and the tool). To my surprise, and through feedback, I realized that you cannot assume that only the flagged area is at risk. Adjacent areas are at risk too! Fires do not follow zone boundaries! I fixed the tool. I built this in 48 hours to close that specific gap: type your address, get a yes/no on whether the NWS polygon covers it, your Genasys evacuation zone, tonight's wind + humidity at your point, a plain-English action checklist, a per-school decision view for East Bay districts, and a one-tap iMessage buddy-check template for a hill-neighbor at 10:30 PM.
Wednesday, 10 June 2026
New top story on Hacker News: Show HN: HelixDB – A graph database built on object storage
Show HN: HelixDB – A graph database built on object storage
19 by GeorgeCurtis | 14 comments on Hacker News.
Hey HN, it’s been just over a year since we launched HelixDB ( https://ift.tt/lO6SRn5 ), a project a friend and I started in college. It’s an OLTP graph database built on object-storage, with native vector search and full-text search (FTS). Why graph, vector and FTS? Graph databases provide a natural cognitive model for data, vectors allow for a semantic understanding of the entities and relationships in the graph, and FTS provides more specific filtering. Many AI-driven applications attempt to combine all of these functionalities by stitching together multiple disconnected systems, but even then there’s no native way to perform joins or queries that span all systems. You still need to handle this logic at the application level. Helix started as a graph DB, but we moved to a hybrid graph/vector approach after attempting to build an AI memory system, which led us down the GraphRAG and HybridRAG rabbit hole, where we would need separate graph and vector databases. We knew scalability would be a challenge at each stage of our product's development, however our initial focus this past year was to prove out the product through local deployments and was only meant to be run on a single node. Scaling graph DBs remained a difficult and expensive problem we’d have to solve later. Some common ways other graph DBs solve scaling is by duplicating entire datasets across distributed machines (extremely expensive per node), or by sharding the data. Sharding databases is effective and affordable, however, graph data doesn’t have explicit partitions like relational databases do. For example, sharding a relational DB involves splitting up tables. When it comes to graph DBs, the edges can span across any of the partitions, and hopping across multiple machines when traversing nodes is ineffective and computationally expensive. Replicating graph DBs for high availability and better throughput drastically increases the operational cost of the db and still has a limit of how big you can vertically scale. The workload that we’re used for requires storing a huge amount of data for agents, where only a subset of that data is ever needed at any one time. So rather than having the whole thing in memory, we can store it all in object-storage and get the bits we need when they’re needed. Agents benefit from better context, which is achieved from more and better data (more relationships etc). By using S3 as the persistence/data layer there is no limit to how big the graph can be or how many relationships you can have, and we can scale to serve throughput and requests by horizontally spinning up nodes and caching relevant subsets of the graph on each node. This way, you get extremely low latency for “hot” data and a p99 of ~100ms for writes and ~50ms for reads from cold storage (S3). Plus you get the benefit of dirt cheap storage. Workloads that HelixDB is currently supporting: - Huge amounts of data (TBs) from which the agents need to search and traverse over - Offering affordable graph storage for companies where cost of graph data is a bottleneck - Consolidating multiple databases, enabling AI agents to have autonomy over companies, helping them become more autonomous. - AI memory - Company brains We’re currently working on our own generalised AI memory layer which will use HelixDB under the hood and be completely open-source. Also, we’re finishing up on pre-filtering for vector search which will allow you to pre-filter based on relationships in the graph, metadata, and sub-graphs. And lastly, GA cloud will be available in the coming weeks. If you want to run Helix locally (either on-disk or in-memory), you can find more info on our github ( https://ift.tt/YGaIlm8 ) or via our docs ( https://ift.tt/QbL0Ykh ). If you’re interested in getting started with our distributed cloud, please email us founders@helix-db.com. Many thanks! Comments and feedback welcome!
19 by GeorgeCurtis | 14 comments on Hacker News.
Hey HN, it’s been just over a year since we launched HelixDB ( https://ift.tt/lO6SRn5 ), a project a friend and I started in college. It’s an OLTP graph database built on object-storage, with native vector search and full-text search (FTS). Why graph, vector and FTS? Graph databases provide a natural cognitive model for data, vectors allow for a semantic understanding of the entities and relationships in the graph, and FTS provides more specific filtering. Many AI-driven applications attempt to combine all of these functionalities by stitching together multiple disconnected systems, but even then there’s no native way to perform joins or queries that span all systems. You still need to handle this logic at the application level. Helix started as a graph DB, but we moved to a hybrid graph/vector approach after attempting to build an AI memory system, which led us down the GraphRAG and HybridRAG rabbit hole, where we would need separate graph and vector databases. We knew scalability would be a challenge at each stage of our product's development, however our initial focus this past year was to prove out the product through local deployments and was only meant to be run on a single node. Scaling graph DBs remained a difficult and expensive problem we’d have to solve later. Some common ways other graph DBs solve scaling is by duplicating entire datasets across distributed machines (extremely expensive per node), or by sharding the data. Sharding databases is effective and affordable, however, graph data doesn’t have explicit partitions like relational databases do. For example, sharding a relational DB involves splitting up tables. When it comes to graph DBs, the edges can span across any of the partitions, and hopping across multiple machines when traversing nodes is ineffective and computationally expensive. Replicating graph DBs for high availability and better throughput drastically increases the operational cost of the db and still has a limit of how big you can vertically scale. The workload that we’re used for requires storing a huge amount of data for agents, where only a subset of that data is ever needed at any one time. So rather than having the whole thing in memory, we can store it all in object-storage and get the bits we need when they’re needed. Agents benefit from better context, which is achieved from more and better data (more relationships etc). By using S3 as the persistence/data layer there is no limit to how big the graph can be or how many relationships you can have, and we can scale to serve throughput and requests by horizontally spinning up nodes and caching relevant subsets of the graph on each node. This way, you get extremely low latency for “hot” data and a p99 of ~100ms for writes and ~50ms for reads from cold storage (S3). Plus you get the benefit of dirt cheap storage. Workloads that HelixDB is currently supporting: - Huge amounts of data (TBs) from which the agents need to search and traverse over - Offering affordable graph storage for companies where cost of graph data is a bottleneck - Consolidating multiple databases, enabling AI agents to have autonomy over companies, helping them become more autonomous. - AI memory - Company brains We’re currently working on our own generalised AI memory layer which will use HelixDB under the hood and be completely open-source. Also, we’re finishing up on pre-filtering for vector search which will allow you to pre-filter based on relationships in the graph, metadata, and sub-graphs. And lastly, GA cloud will be available in the coming weeks. If you want to run Helix locally (either on-disk or in-memory), you can find more info on our github ( https://ift.tt/YGaIlm8 ) or via our docs ( https://ift.tt/QbL0Ykh ). If you’re interested in getting started with our distributed cloud, please email us founders@helix-db.com. Many thanks! Comments and feedback welcome!
Tuesday, 9 June 2026
Monday, 8 June 2026
Sunday, 7 June 2026
Saturday, 6 June 2026
Friday, 5 June 2026
Thursday, 4 June 2026
New top story on Hacker News: Show HN: Cost.dev (YC W21) – making agents cost-aware and cheaper to call
Show HN: Cost.dev (YC W21) – making agents cost-aware and cheaper to call
8 by akh | 1 comments on Hacker News.
We launched Infracost on HN five years ago ( https://ift.tt/RBbiNW3 ) where our CLI generated cost estimates for infra-as-code, e.g. "this Terraform PR adds $400/mo". The idea was to shift cloud costs (FinOps) left, so engineers get visibility of costs before deployment and make better decisions. Earlier this year we started seeing agent traffic in our logs and it looked like coding agents were calling our CLI. But that CLI wasn't designed with coding agents in mind. We went down a philosophical rabbit hole to see if a CLI is even needed anymore given that Claude, Copilot et al. already follow best practices. Ultimately we decided to create a new CLI from the ground up with coding agents in mind for two reasons: 1. We optimized the CLI for agent callers and cut Claude's output token usage by up to 79% and API cost by up to 67% versus a bare-Claude baseline. We wrote a blog documenting our lessons on optimizing user token usage when designing a CLI, e.g. using predicate flags so the agent doesn't compose jq | python | wc pipelines, output format that strips JSON's redundant field names. The blog is here: https://ift.tt/vSyTnNb... 2. With cloud costs, precision matters. Telling a coding agent "make this Terraform cost-optimized" can be expensive and lossy. You burn tokens loading code and policy context into every conversation. Your agent could make up a price and you wouldn't know because it's difficult to verify that across the ~10M price points that AWS, Azure and Google have. The CLI runs static analysis on the code, uses the latest prices from cloud vendors, and passes that context to the coding agent. So that's what we're launching today - Cost.dev: https://cost.dev/ . - It runs locally. Your code never leaves your machine, you get a fast feedback loop, and you're not burning API calls per character when you want to fetch prices. - The CLI does the deterministic work. Fetching price points, scanning the code, validating fixes. The coding agent does the natural-language part. You don't have to trust the LLM to remember the rules, and can verify it called the right CLI command. - It provides a consistent rule layer across every tool you use. Get cost estimates in your IDE and your coding agent with a single install. We support Claude Code, GitHub Copilot, Cursor, Windsurf, OpenAI Codex, Gemini CLI, as well as IDEs like VS Code and JetBrains Before we keep building more in that direction, I want to sanity-check with HN: is "agents writing IaC in prod" actually a thing yet, or am I betting on a future that's still a year out? I know software developers are using coding agents heavily, but are platform/infra folks doing that for prod too? Also, if you have any feedback on Cost.dev, I'd love to hear it!
8 by akh | 1 comments on Hacker News.
We launched Infracost on HN five years ago ( https://ift.tt/RBbiNW3 ) where our CLI generated cost estimates for infra-as-code, e.g. "this Terraform PR adds $400/mo". The idea was to shift cloud costs (FinOps) left, so engineers get visibility of costs before deployment and make better decisions. Earlier this year we started seeing agent traffic in our logs and it looked like coding agents were calling our CLI. But that CLI wasn't designed with coding agents in mind. We went down a philosophical rabbit hole to see if a CLI is even needed anymore given that Claude, Copilot et al. already follow best practices. Ultimately we decided to create a new CLI from the ground up with coding agents in mind for two reasons: 1. We optimized the CLI for agent callers and cut Claude's output token usage by up to 79% and API cost by up to 67% versus a bare-Claude baseline. We wrote a blog documenting our lessons on optimizing user token usage when designing a CLI, e.g. using predicate flags so the agent doesn't compose jq | python | wc pipelines, output format that strips JSON's redundant field names. The blog is here: https://ift.tt/vSyTnNb... 2. With cloud costs, precision matters. Telling a coding agent "make this Terraform cost-optimized" can be expensive and lossy. You burn tokens loading code and policy context into every conversation. Your agent could make up a price and you wouldn't know because it's difficult to verify that across the ~10M price points that AWS, Azure and Google have. The CLI runs static analysis on the code, uses the latest prices from cloud vendors, and passes that context to the coding agent. So that's what we're launching today - Cost.dev: https://cost.dev/ . - It runs locally. Your code never leaves your machine, you get a fast feedback loop, and you're not burning API calls per character when you want to fetch prices. - The CLI does the deterministic work. Fetching price points, scanning the code, validating fixes. The coding agent does the natural-language part. You don't have to trust the LLM to remember the rules, and can verify it called the right CLI command. - It provides a consistent rule layer across every tool you use. Get cost estimates in your IDE and your coding agent with a single install. We support Claude Code, GitHub Copilot, Cursor, Windsurf, OpenAI Codex, Gemini CLI, as well as IDEs like VS Code and JetBrains Before we keep building more in that direction, I want to sanity-check with HN: is "agents writing IaC in prod" actually a thing yet, or am I betting on a future that's still a year out? I know software developers are using coding agents heavily, but are platform/infra folks doing that for prod too? Also, if you have any feedback on Cost.dev, I'd love to hear it!
New top story on Hacker News: Fear and Social Pressure Are 'Overarming' the U.S.
Fear and Social Pressure Are 'Overarming' the U.S.
15 by achristmascarl | 6 comments on Hacker News.
15 by achristmascarl | 6 comments on Hacker News.
Wednesday, 3 June 2026
New top story on Hacker News: Launch HN: Hyper (YC P26) – Company brain to power agentic development
Launch HN: Hyper (YC P26) – Company brain to power agentic development
16 by shalinshah | 6 comments on Hacker News.
Hey HN, we’re Shalin & Kanyes, best friends who've been hacking together for 10+yrs, and now founders of Hyper ( https://heyhyper.ai/ ). Hyper is a shared “company brain” that plugs into information flowing inside a company to make AI agents and automations better and ultimately save people time. Models have gotten good enough that they can (mostly) take on long-horizon, complex tasks. We believe the bottleneck now is that these smart-enough models often lack information about your company, which is scattered in people's heads, Slack threads, stale docs, and in back-and-forth convos with AI. MCP is useful for getting some info in front of an agent, but there are problems: (1) Once the session dies, so does the insight, so instead of copy-pasting a whole doc each time you're telling the agent to dig through Drive each time - not much of a win; (2) Even when MCP works, what it gathers isn't comprehensive, because people decide things on a whiteboard, brainstorm out loud, post a little in Slack, and scribble the rest in a doc, which leaves the agent working from partial information; (3) And even if it had everything, it doesn't do the meta-reasoning required to do a great job. If you paste in a Notion doc and it won't learn your design taste or your writing style unless you tell it to, and it won't know why a decision was made or when. As undergrads 5 years ago, we were into the tools-for-thought wave and became power users of Notion, Obsidian, Roam, Anki, real believers in building a second brain. After GPT-3.5 came out we started to realize how much more powerful that second brain could be if an AI could actually read it, because suddenly it would know our backstory, our taste, our preferences, and unlock genuinely new capabilities. That’s why we’re building Hyper. We know it’s not for everybody! But for people who do want to be on the cutting edge, this is a force multiplier that makes agents faster and better. It increases the number of tasks they can do, and how effectively they do them. Hyper works by ingesting everything you give it access to, Docs, Slack, Email, Calendar, Granola, and synthesizes it into a knowledge graph of facts and their relationships with embeddings for semantic search. The memory system we’ve built is hybrid, with two modalities. Episodes are the raw source items kept as the source of truth. Facts are the meaning pulled out of each episode, stored as subject-predicate-object records with a plain summary and timestamps for when the fact was introduced and when it was invalidated (subject=person, predicate=works_at, object=company). Facts form a graph with typed edges between them: X is in tension with Y, A is derived from B, J supersedes K. Every time a new fact comes in we update the facts in its neighborhood, so the graph stays current, and that's how we handle stale information. When "we'll ship Friday" is later contradicted by "we're shipping Monday," the new fact supersedes the old one instead of both looking equally true, and we never auto-discard the superseded version, so you can still ask how you landed on Monday. Every fact carries provenance back to its source and access-control tags for who is allowed to see it. At retrieval we query-expand, then fuse semantic search over embeddings with Postgres full-text search using reciprocal rank fusion, and we only ever evaluate a query against the facts and episodes that person has access to, which means two people on the same team can ask the same question and get different answers. We keep information fresh with webhooks where they exist and polling where they don't, hashing contents to catch changes for sources that don’t handle native dedupe. Agents read and write through two paths: lifecycle hooks in tools like Claude Code, Cowork, Codex, and Cursor, where we inject relevant context on every prompt and pull interesting facts out of every response, and plain MCP tool calls for everything that doesn't expose hooks. We love it! and so do our early users: one CEO uses Hyper to draft emails in his voice with full company context. What took hours/week now takes minutes and gets sharper each time Hyper learns more how he thinks and how his company is changing. Another YC founder one-shotted a launch video script because Hyper already knew their product, voice, positioning accumulated over months. We have a 3-day free trial, explained more on our pricing page ( https://ift.tt/6Bpcn9M ) and there are more details in our FAQ ( https://heyhyper.ai/faq ), including things like privacy, compliance, and how we’re different from other “memory” companies.. Give it a spin! break it! and tell us where it falls short: https://heyhyper.ai/ . We'd love to build you a 10-star experience :) Comments welcome!
16 by shalinshah | 6 comments on Hacker News.
Hey HN, we’re Shalin & Kanyes, best friends who've been hacking together for 10+yrs, and now founders of Hyper ( https://heyhyper.ai/ ). Hyper is a shared “company brain” that plugs into information flowing inside a company to make AI agents and automations better and ultimately save people time. Models have gotten good enough that they can (mostly) take on long-horizon, complex tasks. We believe the bottleneck now is that these smart-enough models often lack information about your company, which is scattered in people's heads, Slack threads, stale docs, and in back-and-forth convos with AI. MCP is useful for getting some info in front of an agent, but there are problems: (1) Once the session dies, so does the insight, so instead of copy-pasting a whole doc each time you're telling the agent to dig through Drive each time - not much of a win; (2) Even when MCP works, what it gathers isn't comprehensive, because people decide things on a whiteboard, brainstorm out loud, post a little in Slack, and scribble the rest in a doc, which leaves the agent working from partial information; (3) And even if it had everything, it doesn't do the meta-reasoning required to do a great job. If you paste in a Notion doc and it won't learn your design taste or your writing style unless you tell it to, and it won't know why a decision was made or when. As undergrads 5 years ago, we were into the tools-for-thought wave and became power users of Notion, Obsidian, Roam, Anki, real believers in building a second brain. After GPT-3.5 came out we started to realize how much more powerful that second brain could be if an AI could actually read it, because suddenly it would know our backstory, our taste, our preferences, and unlock genuinely new capabilities. That’s why we’re building Hyper. We know it’s not for everybody! But for people who do want to be on the cutting edge, this is a force multiplier that makes agents faster and better. It increases the number of tasks they can do, and how effectively they do them. Hyper works by ingesting everything you give it access to, Docs, Slack, Email, Calendar, Granola, and synthesizes it into a knowledge graph of facts and their relationships with embeddings for semantic search. The memory system we’ve built is hybrid, with two modalities. Episodes are the raw source items kept as the source of truth. Facts are the meaning pulled out of each episode, stored as subject-predicate-object records with a plain summary and timestamps for when the fact was introduced and when it was invalidated (subject=person, predicate=works_at, object=company). Facts form a graph with typed edges between them: X is in tension with Y, A is derived from B, J supersedes K. Every time a new fact comes in we update the facts in its neighborhood, so the graph stays current, and that's how we handle stale information. When "we'll ship Friday" is later contradicted by "we're shipping Monday," the new fact supersedes the old one instead of both looking equally true, and we never auto-discard the superseded version, so you can still ask how you landed on Monday. Every fact carries provenance back to its source and access-control tags for who is allowed to see it. At retrieval we query-expand, then fuse semantic search over embeddings with Postgres full-text search using reciprocal rank fusion, and we only ever evaluate a query against the facts and episodes that person has access to, which means two people on the same team can ask the same question and get different answers. We keep information fresh with webhooks where they exist and polling where they don't, hashing contents to catch changes for sources that don’t handle native dedupe. Agents read and write through two paths: lifecycle hooks in tools like Claude Code, Cowork, Codex, and Cursor, where we inject relevant context on every prompt and pull interesting facts out of every response, and plain MCP tool calls for everything that doesn't expose hooks. We love it! and so do our early users: one CEO uses Hyper to draft emails in his voice with full company context. What took hours/week now takes minutes and gets sharper each time Hyper learns more how he thinks and how his company is changing. Another YC founder one-shotted a launch video script because Hyper already knew their product, voice, positioning accumulated over months. We have a 3-day free trial, explained more on our pricing page ( https://ift.tt/6Bpcn9M ) and there are more details in our FAQ ( https://heyhyper.ai/faq ), including things like privacy, compliance, and how we’re different from other “memory” companies.. Give it a spin! break it! and tell us where it falls short: https://heyhyper.ai/ . We'd love to build you a 10-star experience :) Comments welcome!
Tuesday, 2 June 2026
Monday, 1 June 2026
New top story on Hacker News: Superintelligence: The Idea That Eats Smart People (2016)
Superintelligence: The Idea That Eats Smart People (2016)
20 by thoughtpeddler | 3 comments on Hacker News.
20 by thoughtpeddler | 3 comments on Hacker News.
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