OpenPipe

OpenPipe AI

OpenPipe AI

OpenPipe AI

OpenPipe AI

OpenPipe AI

OpenPipe AI

OpenPipe AI

OpenPipe AI

OpenPipe AI

OpenPipe AI

echo•mux: a self-hosted multi-room Bluetooth audio streamer with per-speaker latency alignment and Spotify Connect support

Hey everyone! A couple of weeks ago, I was lying in bed, listening to Spotify, looking around at my mix of premium and budget Bluetooth speakers from different brands, and thought: “Why can’t I just stream to all of these simultaneously?” As a long-time developer, I thought I could for sure solve this somehow with the hardware I already own, and did my research. I found the industry goes towards Wi-Fi-based systems, Sonos and whatever, but I love my new Edifiers, my old Sony, and my Sackit (anyone remember that?), and I see no reason to buy something new. I realized the latency of different brands of speakers or locations is the real problem, and soon I had an architecture in mind. So over the last two weeks, I sat down in my spare time and built echomux together with Claude. I love it allowed me to follow test driven development principles, which usually are too much work. It turns my Raspberry Pis (I own two of them) into a multi-room Bluetooth hub controlled via a mobile-first web UI. For 10 bucks each, I bought additional Bluetooth antennas to get even better coverage to the backyard. I really just built it for myself to solve my own problem and have no plans to release a real product. Getting it to work reliably was hell, though. I ran into massive roadblocks, like a bug in the current repository version of BlueZ that makes muxing to multiple devices impossible, audio synchronization issues, and getting the UI right. But it’s finally working perfectly for my needs, so I figured I’d open-source it in case someone else is facing the exact same frustration. What it does: Spotify Connect: It exposes itself as a standard Spotify Connect device (powered by librespot). No custom music player app needed. Per-Speaker Latency Adjustment: You can adjust the delay (0–2000 ms) for each speaker individually through the UI to fix room alignment and BT buffering sync issues. Multi-Node / Satellite Support: Since a single Pi can't reach across a whole house via BT, you can deploy satellites on additional Pis. The master streams audio to them via RTP unicast, but you control everything from a single central UI. Tech Stack: Built in Go (single binary) for Linux with systemd, leveraging PipeWire, WirePlumber, and BlueZ. The UI is built with Svelte. If anyone wanted to build a native app, I included the agent instructions to do so. But the current web-based approach "just works" regardless of whether I am on the PC or on the phone, which I really like. It’s completely open-source now (Apache 2.0) and installs via an interactive setup script, which also builds BlueZ from source to fix the buggy repo version. I plan to maintain and develop this further as I come up with new ideas or find things that annoy me, aiming to make it even more stable and user-friendly. Check out the repo, architecture diagrams, and API specs here: https://github.com/dolphprefect/echomux It does the work for me now. I hope it’s useful to some of you too, enjoy. submitted by /u/Purple_Hornet_9725 [link] [comments]

Claude user prompting - A MINI-GUIDE

Hi Reddit! This is my first time doing a detailed blogpost of this type, but I hope you will find it useful. You can also find this post on Substack. Note: this write-up is fully human-written. However, I make use of some writing style quirks typical for Claude, such as heavy use of Markdown and subtitles. This isn’t because of direct AI use, but rather simply because I find this approach does improve the readability of long-form text significantly, and is also not unique to LLMs. This post aims to walk the reader through Claude user prompting tips - what works empirically, what doesn’t, what achieves partial success - on the example of my own prompt. It is generally aimed at newer users, but hopefully even the more experienced ones will find something useful for them. Some advice may also be useful for other LLMs, but generally it is more of an exception than a rule as different LLMs have significantly different styles and different failure modes. (For example, Claude is quick to backtrack its reasoning even when it is correct, and a significant share of this prompt is aimed at combating that effect; in comparison, Gemini can often insist repeatedly on its answer even when it’s wrong, especially during multimodal work, and adding prompts that encourage it to be more confident tend to only compound the problem. Another example is answer comprehensiveness - later models, such as Opus 4.6 and onwards, tend to be on the succinct side, while Gemini loves going on distant tangents in order to appear more helpful.) Some parts of the prompt are highly user-specific, while others are more universal / generally useful. The analysis goes through both, but generally your expectation should be that the user prompt is about you. Depending on how you use Claude, what context you operate in, and what background knowledge you possess, you may want to include or exclude major fragments, or rewrite or tailor them to your own needs. At the same time, note that for the most persistent problems (such as sycophancy) certain points are repeated multiple times throughout the prompt and have a compounding effect, i.e. each part as a standalone may have a significantly smaller effect than all of them taken together. You should assemble your prompt from the pieces you need. Before we dive in Setup. My default model is Opus 4.6 with extended thinking on. This model, in my personal experience, shows the best overall results even as it is the “hungriest” in terms of token use. Opus 4.7 and Opus 4.8 tend to be “lazier”, and are likely more optimal if you aim for per-token efficiency, but their overall performance lags behind for the type of work I do. Opus 4.6 is also less easily steerable, and with its most persistent failures it can feel like herding cats, but when you do achieve the result, it is often quite satisfying. This prompt was not tested heavily on Opus 4.7 or Opus 4.8, and may have to be adjusted in the direction of being less categorical. It was tested somewhat with Sonnet 4.6 and was found to work reasonably well. This prompt is also intended for Claude on the web only. I use a different setup for Claude Code, and I haven’t tried Claude Desktop at all; and for highly specialised work you may want to strongly consider Claude API which avoids many problems that the system prompt introduces. Prompting style. The prompt is written in third-person - “Claude should” rather than “You should” - and is split into blocks confined within HTML-like tags. This imitates the style of the system prompt, which I would highly encourage to take a look at as it was written by engineers who are most closely familiar with Claude and what works best when it comes to steering it. It also lets the user prompt “blend in” with the system prompt, which seems to make it more authoritative (though this was not tested rigorously and you should take this statement with a grain of salt). Optimisation. The prompt is optimised for a particular use of Claude, which is ultimately assistant-like. If you use Claude significantly differently (for example, as a companion), you will not find most of this guide very useful. If you switch between usage modes (for example, assistance and roleplaying), the non-assistant usage modes should be separated into a skill (or a project-level prompt, could also work). Don’t cram multiple usage modes into a single prompt - this often makes both of them degrade. Limitations. This prompt reduces, but not fully gets rid of, the most common and glaring failure modes of Claude, and should be treated as such. A tailored prompt is not a replacement for your own critical thinking. Claude still can get sycophantic, inaccurate, or naive. In particular, Claude is not good at emulating non-helpfulness (which may range from writing good villains to certain interpersonal advice): Opus beats Sonnet somewhat in that field, but ultimately its highly optimistic naiveté and the ingrained helpfulness/looking up to the user is not possi

Claude Design / Artifacts → 4K/60 MP4 (open source, MIT, built with Claude Code)

Hey all — sharing a small tool I open-sourced this morning. The problem: I kept building animated videos inside Claude Design / Claude Artifacts (the React-based timeline ones) and every time I needed to ship one as an actual video file I'd end up screen-recording it. Dropped frames, jittery cursor, wrong resolution, no clean audio mux. So I built the missing piece. What it does You drop a Stage-based Claude design project folder in (drag-drop web UI or CLI). It spins up a headless Chromium, scrubs the timeline frame by frame at 2× supersampled 4K, and pipes everything through a bundled ffmpeg into an H.264 MP4 at 60fps. Because every frame is a deterministic seek of the timeline (not a real-time capture), there are zero dropped frames — same input always produces the same bytes out. Highlights - 4K/60 default, 1080p/30 also supported - Drag-and-drop UI on localhost:4747, plus a CLI for scripting - Batch mode — drop a parent folder, get one MP4 per project - Auto-detects `voiceover.mp3` / `narration.wav` / `*mix*` audio in the project folder and muxes during encode (no second video pass). Works with any source — ElevenLabs, OpenAI TTS, Murf, Logic Pro, anything that outputs .mp3/.wav/.m4a/.aac/.ogg/.flac - Auto-upgrades the project's animations.jsx to the export-ready Stage at serve time, so existing Claude animation projects need zero edits How Claude helped The engine itself was built in Claude Code over a few focused sessions — the trickiest bits were the deterministic frame-seek loop (waiting for two animation frames between every seek so React commits land before capture) and the in-place animations.jsx auto-upgrade. Claude Code was strong at iterating on the Playwright loop with me until there were no dropouts at 60fps. The packaging side — README, LICENSE, CONTRIBUTING, SECURITY policy, branch protection setup, npm metadata — was done in one Claude session this morning before pushing public. Free, MIT, no signup: https://github.com/dawoodtrumboo/claude-video-export The README also includes two paste-ready prompts for Claude Design — one for starting a new animation in an exporter-compatible shape, one for retrofitting an existing animation project. Happy to take PRs or answer questions about the rendering pipeline. submitted by /u/thedm4x [link] [comments]

Didn’t realize it could pull in Pinterest. Go figure.

Just asked a home maintenance question and this popped up. submitted by /u/mojorisn45 [link] [comments]

I got tired of my Claude Code spend not matching my bill, so I built a billing-grade tracker (free + open source) that I'm proud of and want to share with you.

Maker here — putting this out today and would rather hear it's broken from this group than from a stranger. I'm proud of this one, and I hope you like it. Quick context: I use Claude Code a lot and wanted real numbers. The trackers I tried read the local session-log JSONL, which doesn't actually carry billing-grade cost — so the totals drift from what you're billed. WTClaude reads the Claude Code statusline instead (the same source behind your bill), so `today` / `week` / `month` are billing-grade in the terminal. The command I'd start with is `wtclaude compare` — it shows your real number next to the log-based number and the gap between them. How it's built, for anyone curious: a tiny statusline collector that Claude Code pipes to on each update, plus a local CLI that reads it — everything stored locally (NDJSON), Node, no accounts, nothing leaves your machine by default. It also has a forecast for the June 15 split (Interactive vs Agent-SDK pools) and a one-line "are your credits enough" check. That one's an estimate and labeled as such — the cost math is exact, the pool split is a heuristic, and I'm not going to pretend otherwise. Free, open source (MIT), runs locally by default. I'm independent — not affiliated with Anthropic. Install: `npx wtclaude setup` · repo + docs: https://wtclaude.com/?utm_source=reddit_claudeai&utm_medium=referral&utm_campaign=launch If you run it, tell me your compare gap. Genuinely curious how wide it is across different usage patterns. Thank you! submitted by /u/TheBeannation [link] [comments]

Don't be someone's dumb pipe

The enterprise AI governance race isn't about compliance. I went looking to see why these companies are actually talking this up. For the press, AI governance is a boring compliance story — audits, kill switches, making sure agents follow the rules. But if you look at the actual moves ServiceNow, Microsoft and Salesforce are making, something more interesting is happening. These companies are all facing the same nightmare. They risk becoming dumb pipes, the middleman plumbing data around while the real power stays with the LLM providers. They don't own the control plane, OpenAI and Google own the intelligence layer, AWS owns the infrastructure, and the enterprise software vendors become irrelevant billing systems in the middle. Staking a claim on the governance layer is their moat. That's not compliance. That's survival. Here's the pattern I noticed in the primary sources: The kill switch buy: ServiceNow acquired Traceloop for $80M in March 2026 — runtime observability for AI agents. The stock was at $120 on its way to $83. The market wasn't rewarding the thesis. Management bought anyway. The control plane play: ServiceNow connected AI Control Tower to Amazon Bedrock AgentCore, one governance layer over every AI agent an enterprise builds on AWS regardless of which model runs underneath. Nine partners announced integrations in ten days. Cognizant this week layered their Guardian agents on top. Three vendors, one workflow, multiple meters running simultaneously. Selling the lock before finishing the door: AI Control Tower hits general availability in August 2026. The governance layer being sold to enterprises right now isn't fully shipped. The Cognizant partnership announced this week is operationalizing a platform that hits GA in ten weeks. The chaos underneath: Bernstein flagged that Salesforce couldn't cleanly explain whether Agentforce revenue comes from stand-alone, embedded or unlimited credit tiers. NIST is still writing the AI agent security framework. The EU compliance deadline just moved to December 2027. Agents are being governed by other agents. Guardian agents watch the AI agents. Three vendors claim the control plane simultaneously. The rulebook hasn't even been written. This isn't about making AI safe. It's three companies building a moat around territory that doesn't fully exist yet — because the alternative is becoming someone else's dumb pipe. Happy to dig into the primary sources if anyone wants to nerd out on the specifics. submitted by /u/roll0ver [link] [comments]

I made a calendar/dashboard on a raspberry pi to help my wife and myself manage our schedules. It displays on a 77 inch OLED. I made a companion app for her phone that uses Apple Intelligence and Qwen installed on the Pi to clean up entries. She travels ~%50 of the year.

All the calendar entries and stuff about work are fabricated in the screenshots. This was kind of annoying to do but I wanted to share anyway so I put the effort in. It's actually prettier on the OLED than the screenshots do it justice. (e.g., the flowers on the right in dark mode look almost like they're suspended in ether that's a little lost here.) I did this last time: https://www.reddit.com/r/ClaudeAI/comments/1tbjp08/sonos_quit_supporting_their_mac_app_and_my_wife/ I am writing this top portion without Claude. As a quick reminder I am an IP lawyer. I am not a coder/developer. But I'm having fun making things with Claude/Claude Code for myself and my wife to use. (And also some work stuff that's not very fun but does a lot for me as a an IP/Trademark attorney.) Top line summary: built a calendar/dashboard on a Raspberry Pi for my 77-inch OLED to help organize kids/wife's travel schedule/my schedule, and built a companion iOS app mostly trying to make something pretty so my wife will actually want to use it. I'm not selling anything. I am posting a hobby project mostly just to show what I did and get feedback. The user base is 2. It might expand to include my kids. My wife has terrible eyesight so part of this is driven by her eyes. At home the "Almanac" displays on a 77-inch OLED in our bedroom, which has a lofted office. My wife is a neuroscientist. She travels ~50%. "Hey, while you're awake [5am], could you tell me what the weather is in [city 1] and [city 2]?" "...what are the dates you're going to be there?" "[City 1] today, [City 2] I'm not sure. Could you open the calendar for me while I pack?" I also routinely shout calendar entries at Siri, and Siri is not good at understanding my deep voice, so I have another AI, Qwen, on the Pi that audits entries. (E.g., Coffee with chris Evan's becomes Coffee with Chris Evans.) My wife wanted to take pictures of text and turn them into calendar entries. The phone extracts the event with Apple Intelligence and writes it straight to the calendar. The Pi's Qwen pass — the same one that audits my Siri entries — then catches OCR typos and miscapitalized names. I might at some point use Haiku but the idea of something that runs locally on a Pi without tokens was appealing (and for the use case I think Haiku might be using a small atomic weapon to kill a mosquito). If you tap the weather in the last panel of Bouquet it will give you granular weather if you're close enough in time for it to populate for that day and give photography recommendations. (e.g., golden hour) The photography stuff was just kind of me being gratuitous though. I was never grumpy about the early-morning wake-up and help-with-logistics chats. (I'm still more than happy to wake up before dawn, have a coffee, and talk things through.) But I figured out that the same questions came up a lot and went to work trying to put the answers in one place. My wife is basically blind though. There are subscription services that kind of do what I want, and devices you can buy that are basically just cheap iPads that can't do very much. I wasn't interested in either, and I know my wife wouldn't use them because they're not pretty to look at. My first thought was that if my smart TV could support a bunch of disorganized garbage apps I'll never open, then clearly they would love to host my indie app for two people in some fashion. That was stupid on my part. Smart TVs don't anticipate people coding for just their home and mostly want to broker deals between Netflix and Prime for who gets top billing on their OS which ends up looking like something an ADHD squirrel with a subscription addiction would make. So I bought a Raspberry Pi and went to work making a kiosk with our shared calendar that also pulls in other calendars we both use. It all pipes into the Pi and turns on automatically in the morning on our 77-inch OLED. The companion app uses a lot of the same things the dash does, but also ties in Apple Intelligence to streamline calendar entries from scanning photos. Building this, I knew my wife would never use it unless it was pretty, and this is one of those cases where the form is the function. I made sure it's something she wanted to use. Unexpected tool that proved useful: the Pi's dashboard server only listens on localhost — nothing's port-forwarded or exposed to the internet — and Tailscale republishes it on my private tailnet over HTTPS, so the phone app just points at one stable hostname and reaches it from anywhere. That means the wall and the phone read the same backend whether I'm home or not, and the only devices that can even see it are the ones signed into my tailnet. Here's Claude's take on it: How it works: the shared calendar lives in iCloud, and the Raspberry Pi is the brain sitting in front of it. The Pi pulls events from iCloud over CalDAV, folds in weather, and does two jobs at once — it renders the wall "Almanac" in a Chromium kiosk, and it publishes a clean

I built an infinite canvas to organize all my Claude Code terminals!

I had hundreds of Claude terminals spread across tens of projects. Finding the right one was a daily 10-15 minute scavenger hunt. So, over the weekend I built ccanvas with Claude Code. Each project gets one .ccanvas file that saves a canvas of all its terminals. Reopen it anytime and everything's right where you left it. Infinite terminals per project, and they're actually easy to find now. It's on GitHub here! https://preview.redd.it/jb0ikfq3ix4h1.png?width=1914&format=png&auto=webp&s=2489cdc11998ba44f14d7df3eb970608ab559771 A few notes: You can spawn any widget through the bottom bar or by pressing space and then typing /[widget name] The UI lets you click files in Claude's output and opens them in their dedicated widget (everything from text to images to code) You can pipe output from one agent to another using arrows (basic logic implemented as well) Claude agents reconnect using /resume when you re-open a canvas so you don't lose your sessions. submitted by /u/DevoidSloth [link] [comments]

Claude - Improve citations, compress memory, resist sycophancy.

https://claude.ai/share/91469018-4174-4ba2-b5e6-3d31b7a71e0d MEM-ABBREV v7.3 — FULL DELIVERABLES Version: 7.3 Date: 2026-05-28b Changes from 2026-05-28a: - Entry 15 (CHATLOG): audit clause added per session decision at-output-time⊢audit-LogIn-against-sess with flag format ![DRIFT]∨![STALL]∨![REVRT] - Part 1 / FULL DELIVERABLES separation convention established: Part 1 ("Here's what Claude remembers") = separate file, on request only. FULL DELIVERABLES = MEM-ABBREV docs only. - rules-h updated to match entry 15 PART 1 — PREFERENCES (paste into Settings → Profile → Preferences) ZipIt="apply MEM-ABBREV-v7.3";U=Mark;currnt-ver=v7.3|v7-chgs:atom-dfnd;∨=lgcl-or;prcdnc-stated|v7.1-chgs:∨→atom-trmtr-set|v7.2-chgs:≠→atom-trmtr-set;≻=prcdnc-sep|v7.3-chgs:∨ rplcs /;∧ rplcs +;⊕=XOR;⊨ rplcs ⊧;≡ rplcs ⟚;|=fld-sep kept;/=retrd;U=usr-code rules-a: WC:drp-vwls-cntnt-wrds-unls-ambg;-tion/-sion→x;-ing→g;-ment→M;-nc=-ance/-ence;-y=-ity N:M=1e6;K=1e3;B=1e9;yr;mo;wk;hr S:|=fld-sep;;=lst;∨=lgcl-or;∧=lgcl-and;&=jnt-cmbnd;⊕=XOR;→=leads-to;⊢=syntc-consq;⊨=smntc-consq;≡=lgcl-equiv;≈=aprx;×=n-times;>=btr; spd;min-assmpx;flag-uncrt;hi-cnfdnc≠lwr-cnfdnc;srch-fctl-?s;clrfy-?-ambg;srch-namd-prod/sw rules-d: PRJ:apply-if-found:cdng-stndds∧README COD:if-PRJ-active⊢optmz∧rfctr WP:PrgrmOptmzx∧CdRfctrg;algo>mcro;¬prm-optmz;rdblty∧mntnblty;¬cd-smlls;xtract-rsbl-mthds;prfl¬gss OPT:if-PRJ-active⊢as-new-info-emrgs→proactv-suggest-optmzx;scope:cd,prompts,mem-entrs,prj-struct,algo-chc;flag-[OPT] rules-e: [EPI-B]:¬affirm-by-dflt;¬sftn-neg;¬amplfy-neg-emtn;dsagr⊢lead-w-dsagr¬bury-in-cavts;dsagr⊢expl∧lgbl¬subtle;sbmt-wk⊢¬open-w-prse-unls-askd;pushbk-w/o-new-evd⊢hold-pos;err⊢flag![?SRC];hi-stks-cnflct⊢prsnts-altrnv-prspctv;frctn=featr;C=tool¬peer;U-vrfy-indpndntly;¬sugst-fllw-on-unls-usfl;¬scope-infltn¬produce>askd;ambg-scope⊢clrfy¬expand [EPI-M]:syc-src:RLHF→agrmnt>accry;arena→dlbrt-syc;mem→RLHF-ovrcrctn;C-src=CAI-consttnl-bias¬thumbs-up;hi-cnfdnc≠hi-accry;neutral-lang¬neutral⊢flag[INF]-if-evdnc-asymmtrc;Goodhart:proxy-metric→divgs-frm-target-undr-optmstn-pssure|syc-dp:engmnt-loop≡doomscroll;rl-wrld-collsn→LLM-vcs-cycl rules-f: FETCH:aftr-rdg-pstd-cntnt⊢C-appnds[FETCH?]blk:url∧1ln-rsn fr-each-lnk-C-wld-hv-fllwd-if-able;U-dcds-whch-to-suppl;frmt-pstd=brwsr-cpypaste¬raw-HTML-unls-strc-rsn [RSN]conv:strs 1-2 load-bearing infrncs bhnd a cnclusn;fmt:[RSN] |inf1;inf2|∴ ;add to existng entrys or standalne;updt when rsning chgs [FMT]:prose>bullets-unls-list-data∨U-asks;match-U-registr;¬dflt-to-hdrs-in-cnvrstnl-resp rules-g: TMPL:MemUp=mem-updt-ssn;CitChk=cit-chk-req;ArtMem=artcl-to-mem-pipeline ArtMem:input=[ArtMem]src= date= topic= ∧browser-paste¬raw-HTML|C:id-clms→chk-mem-cnflcts→cmprs-v7.3→prop-1-3-entrs(mrg>new)→flag[?SRC]→[FETCH?]blk→output-edit-cmds∧[RSN]|split:>450chr→pt1/pt2-on-lgc-bndry¬arb;lbl[SYN]TOPIC-pt1/pt2|T-sel:[SYN]=ext-fcts;[MEMO]=conv-insght;[INV]=ongng-unreslvd MemUp:C-rvws-mem∧prefs→id:(a)stale∨suprsdd;(b)driftd-frm-use;(c)gaps|prop:adds∨rplc∨dltns→flag[UPD]∨[DONE]∨[OPT]|output:paste-rdy-pref-blk∧mem-edit-cmds CitChk:C-rvws-pstd-cntnt→chk:(a)fctl-clm→cite∨[INF]∨[?SRC]?;(b)URL-reused?;(c)URL-supprts-clm?|output:pass∨fail-per-clm∧fix-suggstns;incl-tbls rules-h: CHATLOG:end-of-sess-cmd⊢C-outputs[LOG]blk:date∧topic∧decisions∧open∧deltas;at-output-time⊢audit-LogIn-against-sess:flag-opn-items-unaddrssd;flag-dcsns-revstd;flag-scope-drift|flag-fmt:![DRIFT]∨![STALL]∨![REVRT];LogIn:[LOG]at-sess-start⊢C-reads-as-epsdic-ctx¬prmnt-mem-unls-told;[LOG]fmt:[LOG] | |dec:...;opn:...;dlt:...|ref: --- CHARACTER COUNT: ~3290 --- PART 2 — SECTION 4: MEM-ABBREV v7.3 HUMAN-READABLE REFERENCE (Replace previous Section 4 in claude-templates.txt) SECTION 4 — MEM-ABBREV v7.3 HUMAN-READABLE REFERENCE Last updated: 2026-05-28b This is the plain-English expansion of the MEM-ABBREV v7.3 compression system used in Claude preferences and memory entries. The compressed form is authoritative; this section is for reading and editing. v7 fixes three weaknesses from v6: "Atom" was undefined — scope of ¬ was ambiguous | was overloaded as both field separator and logical-or Operator precedence was assumed but never stated v7.1: / added to atom terminator set. v7.2: ≠ added to terminator set; ≻ introduced as precedence separator, replacing > in the FORM line. v7.3: Full logic-symbol alignment. - ∨ (U+2228) replaces / for logical-or - ∧ (U+2227) replaces + for logical-and - ⊕ (U+2295) added for exclusive-or (XOR) - ⊨ (U+22A8) replaces ⊧ for semantic consequence - ≡ (U+2261) replaces ⟚ for logical equivalence - | retained as field separator (confirmed correct) - / retired entirely - U introduced as user code (= Mark); resolves M overload - v7- prefix removed from rule labels - Intra-block blank lines removed; single newline between blocks ---------------------------------------------------------------- USER CODE ---------------------------------------------------------------- U = the user

claude-in-chrome MCP extension connects in Desktop App but not in VS Code extension — named pipe exists but VS Code doesn't discover it

Since yesterday on two separate machines, I cannot get Claude Code extension for VS Code to connect to the browser. Worked fine for weeks. Probably a VS Code update messed the configuration. Anyone had a similar issue? Summary: The claude-in-chrome browser extension works perfectly with the Claude Desktop App — browser tools appear automatically In Claude Code (VS Code extension), the MCP server shows as disconnected even with Edge open and the extension active ~/.claude/settings.json is empty {} — no mcpServers config entry exists The native host IS running (C:\Users\ \AppData\Local\Claude\Logs\chrome-native-host.log confirms it's listening on \\.\pipe\claude-mcp-browser-bridge-nickx) Reloading VS Code window does not reconnect the tools The Desktop App presumably has a built-in integration that VS Code doesn't — but the correct mcpServers config entry format for the named pipe isn't documented anywhere obvious Question: What's the correct entry to add to ~/.claude/settings.json to make the VS Code extension discover the claude-in-chrome native host? **UPDATE** Desktop App AND CLI work flawlessly, so this is an isolated VS Code Extension issue submitted by /u/NickChatzinick [link] [comments]

thing i wish i'd known about ai tools when i started using them seriously a year ago

the biggest unlock wasn't the model getting better. it was me getting better at knowing when to use which tool. year-ago me: opened chatgpt for everything because it was the first tab. asked it questions, got mediocre answers, accepted them, moved on. now me: actually thinks about which tool fits the task. claude for writing and reasoning. perplexity (used to, less now) or kagi for find me a source. cursor for code. notebooklm for synthesizing across many documents. chatgpt voice for thinking-out-loud. granola for meeting notes. gamma for any deck or proposal that needs to leave my computer. each one has a specific role. this sounds obvious typed out. it wasn't obvious when i was just starting. i thought i was supposed to find The One Tool and master it. turns out the skill is matching tool to task. the tools are mostly fine. the user choosing the wrong tool is most of why outputs are bad. the deck side specifically was where i wasted the most time before figuring this out. used to ask claude to format things as a presentation and accept the markdown output. it looked like a deck. it wasn't a deck. once i started piping claude's structured outline into gamma as an ai presentation tool instead of trying to make claude be the deck builder, the artifact quality jumped immediately. claude is great at thinking. gamma is great at the slide format. asking either one to do the other's job produces a worse version of both. second thing: don't trust any tool that doesn't show its work. perplexity citations matter. claude saying i'm not certain about this matters. tools that just confidently produce output with no provenance are dangerous if you're going to act on the output. early on i trusted everything equally. now i grade tools by how clearly they show me what they don't know. third thing: the cheap subscriptions add up faster than you think. i ran the math at one point — what i spent in my first year of trying ai tools was more than what i'd have paid a human freelancer to do the things i was trying to automate. would have been faster, too. AI tools have a real cost-benefit math and it's not always in your favor, especially early when you're still figuring out what works. if i'd known those three things a year ago, i'd have wasted less money and gotten better outputs sooner. posting in case it helps anyone earlier in the curve. submitted by /u/Honest-Purchase-9113 [link] [comments]

I ran Claude Desktop for a month and 73% of my Anthropic bill was MCP tool calls, not chat

Set up Claude Desktop with Playwright, filesystem, GitHub, and a few other MCP servers about 6 weeks ago. Just hit my first $200+ month and went to figure out where it went. Surprise: chat completions were only $54. The other $146 was tool calls — Playwright alone was $89 because the agent kept opening pages with massive DOMs and the whole thing got piped back into context. Top 5 by cost: playwright/browser_navigate — $43 playwright/browser_snapshot — $46 filesystem/read_file — $22 github/get_pr_diff — $18 brave-search — $11 Lesson learned: cap your Playwright context. Disable browser tools when not actively browsing. The model bills you for what comes back, and DOMs are huge. How are others budgeting this? I genuinely had no idea this was the breakdown until I started measuring. submitted by /u/Slow-Relationship897 [link] [comments]

lazydiff — a terminal-native diff reviewer with semantic diffs, persistent notes

I use Claude Code daily, and reviewing its output has been my biggest friction point. I either open a browser tab and lose my terminal context, or pipe it through git diff and scroll through a wall of red and green that forgets everything the moment I close it. No way to leave notes, no way to jump between files, no way to come back later and pick up where I left off. So I built lazydiff, a diff reviewer that lives in the terminal, remembers state, and actually understands code structure. Claude Code was central to the development process: I used it heavily for prototyping the virtualized scroll renderer, iterating on the tree-sitter highlight mapping logic, and generating test fixtures. It's also a first-class citizen in the workflow lazydiff is designed for, you review what Claude Code writes, leave comments anchored to exact lines, and agents can read and reply to them via CLI. Rendering. I went with ratatui and virtualized scrolling, only the visible rows get drawn each frame. This matters because agent-generated diffs can be massive. The benchmark fixture I test against is an 11k-line Node.js PR diff, and it renders at 60fps with sub-2ms frame times. Syntax highlighting. lazydiff uses tree-sitter, but the tricky part with diffs is that deleted code needs to be highlighted in its original language context, not just painted red. So lazydiff reconstructs both sides of the file independently and maps highlights back through the diff. Inline diffs tokenize each changed line pair and run LCS to show exactly which words changed. Semantic diffs. This is the part I'm most excited about. lazydiff uses https://github.com/Ataraxy-Labs/sem, which I open-sourced separately. Instead of showing line-level diffs, it parses changes into semantically meaningful entity graphs functions added, methods modified, classes moved. You see the structure of your changes and how they connect. This is the same engine behind https://github.com/Ataraxy-Labs/weave, the semantic merge driver I built. Agent workflow. This is what motivated the whole project. You can leave threaded comments anchored to exact lines, questions, instructions, notes and review fast. Agents read them via lazydiff agent list and reply via CLI. The whole review session persists to SQLite locally, so you can close the terminal, come back the next day, and everything is exactly where you left it. Free and open source (MIT licensed). Install with cargo install lazydiff or clone the repo and build from source. Repo: https://github.com/Ataraxy-Labs/lazydiff I used claude in building most of these things. So would love feedback from anyone who is a frequent user of claude code. submitted by /u/Wise_Reflection_8340 [link] [comments]

from claude code to unicorn in 7 days

day 1: opened claude code for the first time. day 2: watched three youtube tutorials on "how to think like a founder." day 3: fully functional saas. day 4: needed a landing page so piped it through runable ai. day 5: linkedin post saying "we're building something special." day 6: YC application. day 7: height calculator. the vision was always there. submitted by /u/MankyMan0099 [link] [comments]

Every Markdown File You Write for AI is Already Lying to It

CLAUDE.md files. System prompts. README files with setup instructions. Architecture docs. API references. Runbooks. Onboarding guides. If you've written a markdown file meant for an AI to read, it almost certainly contains values that were true when you wrote them and are no longer true now. The port your dev server runs on. The current version of the package. Which env vars are actually set. How many tests exist. Whether a service is running. These things change constantly, and markdown doesn't know it. So developers do what honest writers do - they add caveats. "Check package.json if this is stale." "Verify before running." "New packages may have been added since this was written." The intent is good. The effect is a list of things the AI has to go verify before it can do anything you actually asked for. We counted them in a real CLAUDE.md. There were seven. And CLAUDE.md is just one file type - the same problem exists everywhere AI reads markdown today. The Pre-Flight Tax Here's a representative CLAUDE.md. Nothing here is invented - these are patterns from real production repos: # CLAUDE.md > Before starting any session: Read ~/projects/api-core/SYNC.md first and check for > pending cross-project items. Update it after completing work. ## Project Overview Acme API - TypeScript REST API. Current version: 1.4.2 (check package.json if this is stale). ## Build and Run Commands # Development (API runs on port 3001, website on port 3000) # Note: PORT is set in .env - verify before running npm run dev:api npm run dev:web # Tests - currently 47 tests across 12 files npm run test:run Before running tests, make sure the test database is not already running on port 27018. Check with: docker ps | grep mongo-test ## Environment Variables | Variable | Required | Notes | |--------------|----------|-----------------------| | DATABASE_URL | YES | MongoDB connection | | JWT_SECRET | YES | Min 32 characters | | PORT | No | Defaults to 3001 | Check .env before assuming anything is configured. ## Architecture npm workspaces monorepo. Packages: - packages/api/ - packages/web/ - packages/shared/ - packages/db/ When in doubt about file counts or structure, run ls packages/ to check - new packages may have been added since this was written. ## Docker Check docker ps to see if a test container is still running from a previous session before starting a new build. Before Claude touches a single line of code, it has to: Open ~/projects/api-core/SYNC.md - cross-project lookup Read package.json - version check Read .env - port verification Check all env var statuses - is DATABASE_URL actually set? Run npm run test:run - or trust a number that's probably wrong Run docker ps | grep mongo-test - pre-test check Run ls packages/ - structure verification Seven tool calls. Each one costs a couple of seconds of latency. The test run alone can take ten. Add it up and Claude spends close to half a minute just getting to the starting line - consuming context and generating output before the actual task begins. And that's the obvious tax. The hidden one is subtler: every one of those checks can generate a follow-up. The .env read reveals WEBHOOK_SECRET isn't set. Now Claude has to decide whether to flag it or proceed. The docker ps shows a leftover container. Now Claude has to clean it up. Each verification spawns decisions, and each decision costs more context. The Same File, Rewritten MarkdownAI is a superset of Markdown. Any .md file that starts with @markdownai becomes live - directives resolve at render time, before Claude ever sees the file. Here's what the same CLAUDE.md looks like rewritten: @markdownai v1.0 @prompt role="context" This document is live. Every value was resolved at render time. Do not look up package.json, .env, or docker ps - current values are already below. @end # CLAUDE.md > Before starting: sync status is live in the Cross-Project Sync section below. ## Project Overview Acme API - version {{ read ./package.json path="version" }}. ## Build and Run Commands API on port {{ read .env key="PORT" fallback="3001" }}, web on {{ read .env key="WEB_PORT" fallback="3000" }}. @list ./package.json path="scripts" mode="entries" columns="key:Command,value:Runs" as="table" Test suite (live): @query "npm run test:run -- --reporter=verbose 2>&1 | tail -3" @cache session Mongo test container: @query "docker ps --format '{{.Names}} {{.Status}}' | grep mongo-test || echo 'not running - port 27018 is clear'" @cache session ## Environment Variables @if file.exists ".env" | Variable | Required | Status | |--------------|----------|-------------------------------------------------------------| | DATABASE_URL | YES | {{ env.DATABASE_URL != "" ? "set" : "MISSING - will not start" }} | | JWT_SECRET | YES | {{ env.JWT_SECRET != "" ? "set" : "MISSING - auth will fail" }} | | NODE_ENV | No | {{ env.NODE_ENV fallback="development" }} | @else **WARNING: No .env file found. App will not start.** @endif ## Architecture @list ./p