🛠️ All DevTools

[Other] Open-source memory for coding agents, synced over SSH

[Other] Show HN: misa77 - a codec that decodes 2x faster than LZ4 (at better ratios) I&#x27;ve spent the last few months working on this codec.<p>It has the following characteristics:<p><pre><code> - SOTA decompression throughput in its ratio class - Decent ratios (comparable to LZ4 at high effort levels) - Slow compression </code></pre> Most of the gains can be attributed to reducing branches and making decompression very friendly to out-of-order cores, by using a smart format.<p>Results on the tarred Silesia corpus on Intel x86-64 follow:<p><pre><code> codec decode ratio encode misa77 -0 5219 MB&#x2F;s 42.64% 54.5 MB&#x2F;s misa77 -1 4274 MB&#x2F;s 39.65% 51.2 MB&#x2F;s lz4 2505 MB&#x2F;s 47.59% 371 MB&#x2F;s lz4hc -12 2531 MB&#x2F;s 36.45% 7.31 MB&#x2F;s</code></pre>

[API/SDK] Launch HN: Coasty (YC S26) – An API for computer-use agents Hey HN, we’re Nitish and Prateek, the founders of Coasty (<a href="https:&#x2F;&#x2F;coasty.ai&#x2F;computer-use">https:&#x2F;&#x2F;coasty.ai&#x2F;computer-use</a>). We’re building computer-use agents that can complete workflows inside legacy desktop software and web applications without usable APIs.<p>Developers send Coasty a natural-language task either through our consumer app or through our API, select a machine or browser environment, and any relevant credentials or files. The agent then operates the interface through screenshots, mouse, and keyboard input, verifies the result, and returns a structured run record with screenshots, actions, outputs, and errors.<p>Here is a raw demo of an agent completing a workflow in a legacy application(It’s a mockup): <a href="https:&#x2F;&#x2F;drive.google.com&#x2F;file&#x2F;d&#x2F;1ZghU_3vsAYhHVz1bsvE0pkvZYk7OUnb1&#x2F;view?usp=sharing" rel="nofollow">https:&#x2F;&#x2F;drive.google.com&#x2F;file&#x2F;d&#x2F;1ZghU_3vsAYhHVz1bsvE0pkvZYk7...</a><p>A lot of important software is still difficult to automate. Healthcare teams submit prior authorizations through payer portals, accounting teams enter data into desktop applications, and operations teams move information between internal systems, spreadsheets, and remote desktops. Many of these applications have no API, incomplete APIs, or integrations that take months to build.<p>The usual alternative is RPA, record a sequence of clicks and replay it. That works when the interface and workflow are predictable, but it often breaks when a button moves, a pop-up appears, a page loads slowly, or the application enters an unexpected state.<p>Coasty takes a different approach. The agent observes the current screen, decides what action to take, executes it, and then observes the resulting state before continuing. It does not require DOM access, an accessibility tree, selectors, or an application-specific integration, so the same API can operate browsers, remote desktops, and older Windows applications.<p>A simplified request looks roughly like this:<p><pre><code> run = coasty.runs.create( environment=&quot;vm_123&quot;, task=&quot;&quot;&quot; Open the patient record in the billing portal. Enter the attached authorization data. Do not submit if the member ID or procedure code does not match. Return the confirmation number. &quot;&quot;&quot;, files=[&quot;authorization.pdf&quot;], approval_required=[&quot;final_submission&quot;] ) </code></pre> The response includes the final status, extracted outputs, a replay URL, and a timestamped event log:<p><pre><code> { &quot;status&quot;: &quot;completed&quot;, &quot;output&quot;: { &quot;confirmation_number&quot;: &quot;PA-184392&quot; }, &quot;replay_url&quot;: &quot;...&quot;, &quot;events&quot;: [ { &quot;type&quot;: &quot;verification&quot;, &quot;field&quot;: &quot;member_id&quot;, &quot;result&quot;: &quot;matched&quot; } ] } </code></pre> The API can also pause a run for human approval, retry from a checkpoint, or return control to the developer when it encounters a condition the workflow did not anticipate.<p>We started working on this last summer, because we saw that models were getting better at vision but kept seeing a gap between computer-use demos and the reliability needed for production workflows. Getting an agent to complete a task once is fairly straightforward. Getting it to repeat that task, recover from unexpected states, avoid silently entering incorrect data, and produce evidence of what it did is much harder.<p>We built several layers around the underlying computer-use model. The system tracks the expected state of the workflow, detects when the application has diverged from that state, and can re-plan instead of continuing blindly. Developers can define invariants such as “the patient name must match the source document” or “never submit without approval,” and the agent checks those conditions during the run.<p>Each run happens in an isolated virtual machine. We expose APIs for provisioning environments, uploading files, starting tasks, streaming events, inserting human approvals, and retrieving the full replay and audit trail. Environments can be kept alive across runs when the application has a long login flow or persistent local state.<p>One problem we are still working through is the tradeoff between speed and reliability. The agent can move faster by taking fewer observations and verification steps, but that becomes risky in workflows involving patient records, payments, or regulatory filings. We currently bias toward slower execution with more checks and let developers configure approval points and verification policies.<p>We are initially working with healthcare operations teams because their workflows combine many of the hardest conditions: payer portals, EHRs, PDFs, spreadsheets, remote desktops, and actions where quiet mistakes are expensive. We also expose the same infrastructure through the developer API for teams building their own agents and vertical automation products.<p>We currently charge based on agent runtime and workflow volume, with separate pricing for dedicated environments and enterprise deployments.<p>We’d especially appreciate feedback from people who have built and&#x2F;or used browser agents, RPA systems, desktop automation, or agent infrastructure. We’re curious which parts of the API you would want direct control over, where you would prefer higher-level abstractions, and which failure modes have been hardest in your own automation systems.<p>If you&#x27;ve hit weird failure modes automating software like this, we want to hear about them. We&#x27;ll be here all day answering questions and taking notes!

[Other] Show HN: StyleSeed – a design-rules engine so AI agents stop building generic UI

[Other] Show HN: Leet Robotics: Learn robotics and ROS2 with hands-on courses Hi all,<p>I&#x27;ve just launched Leet Robotics: a platform to learn robotics hands-on, with a full ROS2 workspace that runs in the browser (Jazzy, Gazebo Harmonic, Foxglove, VS Code) - no install required. The platform also has room for sharing projects and simulation assets as it grows.<p>Our first course is live now: Intro to ROS2 (free to read). The course teaches skills ranging from building your first node to a capstone project of a robot touring a museum world, with every lesson runnable in the online workspace (free accounts get an hour of workspace time daily - enough to follow the course).<p>Would love feedback from this community: on the course, the workspace experience, and what courses to build next.

A coding agent for low-cost models

[Other] Show HN: Make senders work to get into your inbox Hi HN :) really excited to share this with you.<p>The one thing AI reliably does is generate noise. Half the tools I see launch are just machines for producing more noise across more channels. And people are starting to see this in the form of emails in their inboxes as spam filters are struggling.<p>There used to be a useful signal in email: the effort a sender put into customizing a message was a rough proxy for how relevant it actually was. AI killed that. Now it&#x27;s customized slop with the appearance of effort with none of the cost. It is painful that the open internet &#x2F; open channels have been abused like this.<p>Captchainbox applies the idea of proof-of-work to email. If a sender is willing to do a bit of work to reach you, the message is more likely to be worth your time and the sender more likely to be real. The work is a traditional captcha. You can also set a pay-to-deliver amount if you want more friction. The proceeds of the delivery payment after transaction costs go to the Internet Archive and the EFF. The tool currently works by authing with your Gmail or Outlook and during launch time I make this completely free as a lifetime deal (with optional payment if you wanna support).<p>How it works: Captchainbox builds a whitelist automatically from the metadata of your past correspondence. If you&#x27;ve emailed an individual address, that sender can reach you. If you talk to several people at the same domain, we whitelist the whole domain. If one transactional-looking sender has sent you more than 10 emails, we treat it as a transactional domain and let it through. This whitelist is for you to change whenever you want. It continues to build organically as you converse with more addresses.<p>Incoming mail is checked against that whitelist. Senders already on it land in your inbox as normal. Anyone else gets archived (never deleted) and is sent a challenge. This can be the captcha or the payment link. Once they solve it, their email is pulled out of the archive and put back into your inbox.<p>if you want to see what this looks like from a sender&#x27;s point of view, send me an email here: doerpfelix15@gmail.com<p>The service only ever reads metadata, never message content. And since nothing is ever deleted, you can&#x27;t lose a message. There is a legitimate risk &#x2F; downside: if you sign up to a new service, these emails also land in the archive. Since we do not process the content, a first-time sender who can&#x27;t solve the challenge (say an automated activation email) will sit in your archive until you spot it.<p>Happy to answer anything! :)

Jurassic Park computers in excruciating detail

Global Warming at 3 °C by 2050? What's Behind the New German Climate Warning

Data centers have hiked electricity prices on the public by $23B

Solving 20 Erdős Problems with 20 Codex Accounts Running in Parallel

Show HN: Tilion – Stealth Browser Infrastructure for Agents

Financing the AI boom: from cash flows to debt [pdf]

Show HN: An Interactive Guide for Castlevania II: Simon's Quest I&#x27;ll start by copying the comment I left on the Reddit post where I originally shared this project and then go into some more detail for this audience. (<a href="https:&#x2F;&#x2F;www.reddit.com&#x2F;r&#x2F;nes&#x2F;comments&#x2F;1umrnd5&#x2F;i_made_an_interactive_guide_for_castlevania_ii&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.reddit.com&#x2F;r&#x2F;nes&#x2F;comments&#x2F;1umrnd5&#x2F;i_made_an_inte...</a>)<p>&quot;&quot;&quot; Castlevania II has always held a special place in my heart. I didn’t own it myself as a kid but I had a family member who did. This meant that I only got a few chances to actually play it, but that left a big impression on me anyway. I recently beat it for the first time and along the way I decided I wanted to build the most over-the-top guide you could imagine as an ode to the game. This is that guide. I hope you all enjoy.<p>Source code: <a href="https:&#x2F;&#x2F;github.com&#x2F;baclap&#x2F;the-ultimate-simons-quest-guide" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;baclap&#x2F;the-ultimate-simons-quest-guide</a> &quot;&quot;&quot;<p>This is a Progressive Web App with full offline support. There are no static images or audio files used to create the guide. What it&#x27;s actually doing is taking real binary data extracted from the ROM at build time, processing it at runtime in WebAssembly, and then rendering it using WebGL2. Similarly the game music is played from ROM-derived data through a Web Audio AudioWorklet that recreates the NES audio channels. There are additional curated JSON files that describe how all this data fits together on the map, the text from NPCs, the secret information, and so on.<p>This was really just a labor of love and something I&#x27;m proud to share. Looking forward to your feedback. Thanks for taking a look!

Show HN: Flashbang – DuckDuckGo bangs resolved locally with a Service Worker I like to use DuckDuckGo-style bangs and snaps, they are fast and efficient shortcuts.<p>However, neither Kagi nor DuckDuckGo resolves them as quickly as I would like and subjectively Google has better search results than DuckDuckGo.<p>After trying a few local alternatives eg. unduck, unduckified, I wasn&#x27;t satisfied, the ones I tried briefly loaded a page before redirecting causing visible page flickering, still took time to resolve the actual redirect and lacked advanced features (address-bar autocomplete). Flashbang avoids that by handling the redirect in a Service Worker, before the browser renders anything. On my machine, the added ove rhead is around 0.14ms.<p>It has 14,470 bangs, custom shortcuts, address-bar suggestions, and works offline once installed (except for suggestions). No runtime dependencies.<p>Try it: <a href="https:&#x2F;&#x2F;flashbang-dyr.pages.dev" rel="nofollow">https:&#x2F;&#x2F;flashbang-dyr.pages.dev</a> Code: <a href="https:&#x2F;&#x2F;github.com&#x2F;ph1losof&#x2F;flashbang" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;ph1losof&#x2F;flashbang</a>

Show HN: Pullrun – Run One OCI Image as a Container, Firecracker, or Apple VM

Launch HN: Agnost AI (YC S26) – Extract user feedback from agent conversations Hey HN, we’re Shubham &amp; Parth, childhood friends building Agnost AI (<a href="https:&#x2F;&#x2F;agnost.ai">https:&#x2F;&#x2F;agnost.ai</a>), product analytics for teams building chat and voice agents.<p>We read production conversations and find behavioral failures like users rageprompting (cursing at the agent), repeatedly rephrasing the same request, correcting the agent, asking for missing features, or leaving after an answer that was technically successful.<p>We have an interactive demo with no signup here: <a href="https:&#x2F;&#x2F;app.agnost.ai?demo=true">https:&#x2F;&#x2F;app.agnost.ai?demo=true</a><p>Here&#x27;s a demo video: <a href="https:&#x2F;&#x2F;www.tella.tv&#x2F;video&#x2F;agnost-ai-launch-hn-demo-9haa" rel="nofollow">https:&#x2F;&#x2F;www.tella.tv&#x2F;video&#x2F;agnost-ai-launch-hn-demo-9haa</a><p>The core problem is that chat and voice products do not have the same metrics as web apps. When the product interface is language, clicks and funnels become much less useful. Users also rarely give explicit feedback, and when they do it&#x27;s usually sugarcoated. I barely type &#x2F;feedback in Claude or Codex myself. Most users just curse, ask again, correct the agent, or leave. So product engineers get technical visibility from latency, errors, and traces, but still have to guess whether users got what they wanted.<p>We got here after building around agents for the last year and got a couple of founders asking for something like a PostHog for conversations for the AI assistants they were building.<p>We are not trying to be in the observability or evals space. Observability tells you what happened technically. Evals validate cases you already know. We&#x27;re more on the discovery side like what users wanted, where they got frustrated, what they asked for repeatedly, and what new evals should exist.<p>Teams send us agent conversation messages through SDKs or OTel, optionally with metadata like account, plan, source, organization, etc. We cluster conversations into product-specific intents. Feature requests and bugs are default categories; most other clusters are created dynamically from the customer’s data and evolve over time. You can create your own cluster in plain English. If a cluster gets too broad, we split it. If a new pattern appears, we suggest it.<p>One AI video editor company used Agnost AI to find feature requests hidden inside chat. The biggest one was that around 70 users wanted auto-subtitles, but users said it as “add this text in this frame” 12x in a single session, “can you caption it”, “give me transcript of audio” and variations across languages. The team later built the feature.<p>Doing this over millions of messages without sending everything to an LLM was the hard part initially. In ClickHouse, “fetch the last 50 events by time across conversations” and “fetch all events in this conversation” want different sort orders, so we had to iterate a lot on sorting keys, partitions, materialized views, and projections.<p>For finding new clusters, sending everything through an LLM was too slow and expensive. HDBSCAN-style embedding clustering also gets painful at scale because of pairwise comparisons. We first split conversations into segments based on cosine drift, run BIRCH to compress the candidate space, and then use HDBSCAN-like clustering on the smaller set. For matching existing clusters, we use embeddings, smaller classifiers&#x2F;BERT-style models, and LLMs only as fallback for ambiguous cases.<p>We’re live with multiple companies and ingesting ~1M chat and voice messages per day. Pricing is public: Starter is free, Pro is $499&#x2F;month, and Enterprise is for higher volume, security, retention needs. We use each customer’s data only for that customer. We are SOC 2 Type 1 compliant, Type 2 is in progress, and our SDKs are on PyPI and npm.<p>We’d love feedback from the HN community and people building chat or voice agents: how do you detect these signals today, what feedback methods have worked, and what would block you from trying this? Happy to answer questions and take criticism.

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Show HN: Opening lines of famous literary works This came from an idea that had been knocking around in my head for several years. I had been collecting opening lines of famous works and thought it would be cool to see one everyday as I opened the browser. I tried different styles but landed on the simple background with the text, let the words speak for themselves. Over time i&#x27;ve added more quotes I believe now there are close to 60, so hopefully you can refresh a few times and get a fresh one every time. I hope you guys like it, enjoy!

Show HN: Cascade Chat – A Hackable IRCv3 Client for macOS, Windows, and Linux Hello HN! I&#x27;m Matt and today I&#x27;d like to show you Cascade Chat.<p>One of my earliest internet experiences was with mIRC. I always admired its straightforward, pleasant UI and the way it wove a hackable core into the code. The way you could build on the visual and API layers of the underlying IRC client to me was fascinating software machinery. It was truly a client that you could build on top of.<p>As my career has progressed, I moved away from Windows and adopted Linux as my daily driver. That was where I found HexChat, the closest thing I could find to mIRC many years later. While I really enjoyed HexChat, it wasn&#x27;t quite what mIRC offered. I eventually found myself on macOS with no clear analogue to either. That&#x27;s why I built Cascade Chat.<p>Cascade is a modern IRCv3 client. It supports persistent local history with full-text search, network management, replies, typing indicators, link previews, pinned messages, native notifications, SASL authentication, server-time, chathistory, account and away tracking, and the ratified IRCv3 capability set.<p>I also wanted Cascade to be hackable, so I built in two fundamentally different layers:<p>- Scripting powered by Go scripts for personal automation, event handlers, and timers. Scripts run in-process with no access to the standard library, filesystem, or network. - Plugins that communicate over JSON-RPC and can be written in any language as external processes.<p>Stack-wise, it&#x27;s a Go application built on top of Wails v3, which leverages the OS&#x27;s native WebView to render modern web tech frontends as desktop applications. The result is an Electron-like experience without packaging a separate Chromium runtime.<p>Full disclosure, since this is HN: I built Cascade using agentic engineering. I made the product and UX decisions, designed the architecture and code interfaces, and I reviewed and dogfooded the resulting work. Coding agents implemented much of the space between those decisions.<p>To further ensure consistent quality, I built gates around that process rather than treating generated code as finished. I focused on unit and integration tests, full-stack end-to-end tests against a real IRCv3 server (Ergo), automated release candidates, and regular dogfooding of the prerelease channel.<p>Cascade is open source under the BSD 3-Clause license. Prebuilt packages are available for macOS, Windows, and Linux. The current builds are unsigned, so macOS and Windows require a first-run confirmation step that I&#x27;ve documented in the README.<p>I&#x27;d love to know if you&#x27;d make Cascade your daily IRC client, and if not, what that&#x27;d take! Feedback and PRs welcome.