Linear

Linear AI

Linear AI

Linear AI

Linear AI

Linear AI

Linear AI

Linear AI

Linear AI

Linear AI

Linear AI

I A/B tested Claude building UI with vs without a design spec (200 apps)

I kept seeing the "Opus is ridiculous for frontend" takes and wanted to know how much of that is the model vs what you feed it. So instead of arguing, I ran it as an eval. Setup: same "clone this screen" task across 200 well-known apps (Spotify, Things, Linear, Duolingo, etc.). Two conditions — (1) prompt + screenshot only, (2) same prompt + a structured DESIGN.md spec (design tokens, spacing scale, component list, states, nav model). Targets: SwiftUI, Jetpack Compose, and Expo. What I found: Iterations to "ship-able" dropped from ~5-6 to ~2 with a spec. Component choice got idiomatic — spec runs used native nav/list patterns; prompt-only runs reached for generic stacks/divs regardless of platform. Biggest delta was consistency across screens. Prompt-only drifts on spacing and type scale screen to screen. Spec-fed stays locked because the tokens are pinned. The model mattered surprisingly little for layout fidelity once the spec was there. It mattered a lot without one. Takeaway: "Claude is good/bad at frontend" is mostly a context problem. The spec does the heavy lifting. I open-sourced the 200 specs I used (MIT, plain markdown, no deps) so you can repro or just drop them into Claude Code: https://github.com/Meliwat/awesome-ios-design-md/ Two questions: Which apps should I add next? Taking requests — that's literally how the list grows. For those of you vibe-coding UI without reading the output (saw the phone post this week) — are you eval-ing the result at all, or shipping on vibes? submitted by /u/meliwat [link] [comments]

GitHub Issue Support on Claude Mobile?

I’ve been trying to figure out if there’s any way I can get Claude on my phone to help me raise issues in my GitHub repo. I know that on desktop I can create an MCP config and that gives me all the fancy tools to raise issues and help me triage them etc. but that doesn’t seem to have a counterpart on mobile. Linear has this kind of connection. Is it possible for GitHub? Is there some kind of feature suggestion to raise or get behind? Would be super useful. submitted by /u/WorriedRobot [link] [comments]

Configured 9 MCP servers in Claude Code over 4 months. Here's the truth nobody tells you about MCP context bloat.

I started loading up MCP servers in Claude Code back in January thinking the more capability the better. I'm at nine now: filesystem, GitHub, Stripe, Linear, Notion, Postgres, Sentry, AWS, and a custom internal one. Total tools across all of them: 142. What nobody warns you about: every one of those tool definitions lands in your context window before any user prompt has been sent. I checked with Claude's tool inspector. Cold start: 38k tokens of system prompt + tool schemas. Every. Single. Turn. The math nobody talks about At ~$15/M output and ~$3/M input on Sonnet, doing 200 turns a day across my agent + Claude Code use: 38k input × 200 turns = 7.6M tokens/day = ~$23/day = ~$700/month JUST in MCP tool definitions This is before any actual work Cache helps but only on identical prefixes; rotate one MCP and the cache invalidates What actually breaks The model gets dumber with too many tools. Not theoretical, watched it myself. With 142 tools in context, Claude started picking the wrong tool for obvious queries (using linear_search_issues when I asked it to read a file). The tools API call itself slows down. Schema-heavy MCP servers (looking at you, AWS) take 4-6 seconds to enumerate. Errors compound silently. One badly-described tool taints the ranking for every related query. What the "MCP optimizer" startups won't tell you Most of them are just BM25 search dressed up. You don't need a vector DB, you don't need an LLM in the loop to rank tools. Tool descriptions are short, structured, and full of keyword matches. BM25 over a flat projection of name + description gets you 90% of the win, deterministically, in microseconds, and offline. The other thing: "replace" beats "suggest" every time. If your gateway hands the model 5 tools instead of 142, the math works. If it suggests 5 alongside 142, the model still loads 142 and you saved nothing. What I do now Switched to a gateway pattern. Claude sees three tools: search_tools, invoke_tool, auth. Everything else gets ranked on-demand. Cold start dropped from 38k to ~4k. Wrong-tool selections basically disappeared because the model only ever sees the top 5 ranked by query. Specifically running Ratel (open source, in-process Rust lib, BM25 ranking, one command does the Claude Code import). Not the only one in the space but the only one with the architecture I actually wanted. Set it up in 10 minutes. Anyone else hit the same MCP wall? Curious what other folks are doing, especially people running 5+ servers in production. submitted by /u/AbjectBug5885 [link] [comments]

Built an MCP for claude code that turns ticket-mentions into PRs with browser QA (and what I learned along the way)

notesasm is an MCP server you add to claude code. you mention a fix mid-flow ("make a ticket on notesasm: fix the regex for quoted emails") and it files the ticket. later, on your schedule, an autonomous agent picks the ticket up, writes the fix, runs real-browser QA against your preview deploy, and opens a PR with screenshots. closed alpha, free during it. demo + signup: notesasm.com the pain it solves (3 separate ones, actually): claude code is fast enough now that shipping isn't the bottleneck anymore. when you're deep in a feature and notice "the regex misses RFC-quoted local parts" or "the footer copy is wrong on mobile", you'd never break flow to open jira/linear or even write it down anywhere. so the idea goes nowhere. multiply by a year and your repo has invisible debt nobody's tracking. claude code helps while you're at the keyboard. it doesn't help while you sleep. your repo doesn't move overnight unless you stayed up to push it. for solo founders or small teams, that means losing 8 hours a day where you could be shipping if you had a way to delegate work to your own agent. and even if you do have something pushing code for you overnight, you lose context with AI-generated PRs and they usually need visual review. claude writes code that compiles and tests pass, but the actual rendered output might be subtly broken (or super broken lol). reviewing those visually is tedious and a lot of teams skip it, then ship regressions. how it works: you add the MCP server: claude mcp add notesasm --scope user --transport http -H "Authorization: Bearer ". BYOK style, the token comes from your dashboard. zero local install beyond the one command. then in any claude code session you can say "make a ticket on notesasm for this" (based on your conversation) and it just files it. the MCP server is HTTP-transport (not stdio), runs in the cloud, hits a fastapi backend that stores the ticket in postgres against your workspace. later (your schedule, your spend cap), a worker process picks up queued tickets. for each one: clones your repo with a github app installation token (commits look like asmnotes[bot], a verified author. bypasses vercel/netlify deploy protection that rejects unknown-team-member commits.) runs the claude agent sdk with your ticket body as the prompt. defaults to sonnet 4.6, opus 4.7 for hard tickets the user marks explicitly. agent reads the codebase, makes the edits, commits, pushes a branch, opens a PR via the github API. waits for your preview deploy to land. vercel polled by default, configurable probe URL for split frontend/backend setups like vercel + railway. QA agent drives a real chrome session on browserbase against the preview. stealth profile with residential proxies. takes before/after screenshots. verifies your acceptance criteria against the rendered output. if QA fails, the report feeds back into the build agent for up to 3 retry iterations before parking the ticket. final: PR with QA screenshots in the description, ready to merge. stack: - backend: fastapi + asyncpg + railway - frontend: vanilla html/js, no build step, vercel - agents: claude agent sdk (build), claude + browserbase (QA) - auth: clerk - email: resend (welcome, invite, feedback) - mcp transport: http (cloud-hosted, no local install) things i learned building it that other claude code folks might care about: - the build agent loves to spawn subagents via the Task tool. disable it explicitly in the system prompt or you get 4-minute hangs the SDK doesn't surface as errors. - browserbase sessions default to a ~5-min timeout. if your QA wall budget is anywhere near that, set the session lifetime explicitly to 1800s on session create (the timeout field). otherwise you get random "410 Gone" mid-run. - don't rely on the SDK's wall budget alone. add a per-message timeout (90s works) so a hung tool call doesn't silently burn your whole budget. - claude code's default mcp scope is per-cwd. always tell users `--scope user` in your install instructions, otherwise the MCP works in one repo and silently doesn't in others. - ResultMessage emissions happen multiple times per job if you have iteration loops (build + QA + qa-fix). sum them all when computing per-job cost, not just the last one. what's next: closed alpha is open. would love ~30 active users to try it out, all free during it. paid plans later this year with a permanent discount for alpha users. happy to answer anything about the MCP design, the QA verification loop, cost tracking, the agent-sdk integration, or anything else. demo + signup: notesasm.com submitted by /u/FormExtension7920 [link] [comments]

Feature request: “Digress” / conversation branching for LLM chats

I would love to see a first-class way to branch, collapse, revisit, and optionally promote side conversations inside an LLM chat. Right now, most chat interfaces treat conversations as one long linear timeline. That works for simple Q&A, but it breaks down when using an LLM for real projects, research, learning, or planning. Human thought is not always linear, and neither is human conversation. When working through a main topic, random but useful side questions naturally come up, just as they do in real-life conversations. Someone may be explaining something, then one word or idea sparks a side discussion. After exploring that side topic, someone eventually says, “but to get back to your question,” and the conversation returns to the original point. That is essentially a real-life digression being collapsed. LLM chats should support that same natural flow without turning the main conversation into a giant messy scroll. For example, I may be working on a Street Sweeper app project involving ESP32 boards. While discussing hardware, I might suddenly want to better understand flash memory, RAM, ROM, or why old video game cartridge files are called ROMs. That side topic is interesting and useful, but it is not directly part of the main Street Sweeper project. It should not permanently clutter the main conversation. My request is a button that lets users “Digress” or “Fork” from a specific point in a conversation. The digression would open as a side branch where I can explore the random thought, ask follow-up questions, and learn what I need. When I am done, I can collapse that branch and return to the main conversation, keeping the main thread clean and focused. Later, I should be able to reopen that collapsed digression. If the side topic becomes important enough, I should also be able to promote it into a brand-new conversation. That way, a small curiosity can grow into its own full chat without forcing me to scroll back through the original project conversation or manually copy and paste everything. Possible actions: Main thread = source of truth Digress = branch off from a specific point Collapse = hide the side branch and return to the main topic Reopen = revisit the side topic later Promote to new chat = turn the digression into its own conversation Attach to project = optionally save that branch as part of a larger project workspace This would make LLM conversations feel less like endless scrolling text and more like organized thinking. It would help users keep serious project chats clean while still allowing natural curiosity, learning, and exploration. I personally like the word “Digress” because that is exactly what the user is doing. In normal speech, people say “but I digress” when they go off-topic and then return to the point. This feature would turn that natural behavior into a clear UI pattern. Chat is currently designed like a straight line, but thought and conversation often behave more like a tree. LLM interfaces should support that. submitted by /u/Dellis251984 [link] [comments]

Personal tool for managing AI coding sessions across the board with some git features...

Started working on this last week since I found myself jumping vscode sessions, terminals and other windows too much and it cost a lot of time/mental energy finding sessions again where i left of or that need attention... Some key features: Multi-repo workspace — all your projects in one dashboard, not one window per repo Worktree-first — spin up a worktree per task/agent without losing track AI agent sessions built in — Claude Code, Codex, and other TUIs run inside the dashboard with live status Activity overview — see at a glance which sessions are working, waiting, or idle Unread badges + favicon alerts — know which session is waiting on you without tabbing through everything Sticky notes — pin thoughts to sessions, mention other sessions/files, build context without leaving the dashboard Custom per-session links — pin the Linear ticket, PR, or docs page next to the session Editor-agnostic — opens your existing editor, doesn't replace it Local-first — workspace is just a git repo on disk, no cloud required Could be OSS if there's interest... but right now it's really made for me and only tested on OSX (altough I try to keep crossplatform in mind since my other main dev machine is windows) submitted by /u/marwi1 [link] [comments]

Built a tool that turns websites into structured design docs for AI workflows

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. Happy to Share Link -- submitted by /u/hiehie [link] [comments]

PM running Notion MCP for 3 weeks. Should I add Linear too or is that overkill?

PM at a 60 person SaaS, not technical. got the Notion MCP server running 3 weeks ago after a friend walked me through it. the unlock has been bigger than I expected. I can ask claude code "what did we decide about the onboarding redesign across our last 4 meeting notes" and it actually reads them and answers. saved me 4+ hours of scrolling already. current setup: ● daily standup notes go into a notion db ● PRDs live in a different notion folder ● meeting transcripts auto-pipe in via fireflies with the MCP I can query across all three. asked claude this morning "did anyone raise concerns about the auth flow change in the last 2 weeks" and it pulled the exact comment from a meeting 9 days ago. felt like magic until I remembered it was just text search with extra steps. now I'm wondering if I should hook up Linear via MCP too. would be nice to ask "what tickets are blocked because of decisions we havent made yet" and have it cross-reference notion notes against linear status. but I'm worried adding another MCP makes responses slower or more confused. is it overkill for a non-coding PM? or is the value worth the setup pain? second question. anyone running 3+ MCP servers at once and finding context bleed? sometimes I worry claude doesnt know which source to trust. would love to hear from PMs specifically because most MCP content I find is engineer-focused and I'm trying to figure out the workflow for non-coding workflow people. submitted by /u/SetGuilty7210 [link] [comments]

Scaling LLMs horizontally: hidden-state coupling without weight modification [R]

Residual Coupling (RC) connects frozen language models in parallel using small, learned linear bridge projections. These bridges read hidden states from one model and inject additive updates into the residual stream of another at intermediate layers. In bilateral setups, simultaneous return bridges form a feedback loop that stabilizes both streams without altering base weights. This architecture establishes a two-step paradigm where base models function as memorizers, while lightweight linear bridges handle cross-domain generalization. Constraining the bridges to purely linear maps prevents overfitting because they can only map existing geometric relationships between the frozen representation spaces. As the bridges are optimized against ground-truth target data, they have no incentive to map ungrounded features such as individual models' hallucinations. Keeping the base weights completely frozen eliminates catastrophic forgetting. The system maintains operational closure, transforming inputs through its existing structure rather than changing to accommodate them. Evaluating bilateral RC against Mixture-of-Experts (MoE) routing across the same frozen models shows these results: Medical (3-model): Reduces perplexity to 11.02, compared to 56.80 for MoE and 57.08 for the frozen baseline. This represents an 80.7% reduction. TruthfulQA Health (MC1): Improves accuracy by 9.1 percentage points over the baseline. Independent models have uncorrelated hallucinations, allowing the bridge gates to amplify consistent cross-model updates while suppressing individual errors. Coding Test: CodeGPT-small-py and GPT-2 use different tokenizers, causing a 7-million baseline perplexity on mismatched text. MoE reaches 878, but RC achieves 5.91 by reading hidden states before the output projection collapses. This framework introduces a horizontal scaling axis for multi-model systems, moving beyond vertical scaling via larger monolithic models. Latency remains bounded by the slowest single model. Specialists can be added or removed without retraining the remaining system. In some scenarios, this architecture could replace multi-turn text prompting in agentic workflows with a single parallel forward pass, allowing models and/or bridges to run on separate nodes or edge devices without a central bottleneck. By decoupling memorization from relational alignment, RC bridges provide a framework for scaling multi-model systems and offer a path toward native multi-modal integration. Paper: https://ssrn.com/abstract=6746521 Code: https://github.com/pfekin/residual-coupling/ submitted by /u/kertara [link] [comments]

Experimenting with screenshot + DOM analysis for better UI understanding

Been experimenting with a tool that converts websites/screenshots into structured design documentation. The original problem was that screenshots alone weren’t enough for reliable UI understanding inside AI/browser-agent workflows. So the tool tries to combine: visual hierarchy DOM/CSS structure spacing systems typography patterns interaction behavior reusable component analysis The interesting part is seeing how different products structure their UI systems internally. Still early and improving daily, but curious what people here think would make something like this genuinely useful in AI/dev workflows. submitted by /u/hiehie [link] [comments]

model-agnostic sensitivity approximator [P]

(to preface, i'm 16 and this is the first package i've ever built. any feedback would be appreciated!) what i've noticed is that most industry-standard xai tools (think shap/lime) focus on feature attribution (why did the model made this prediction), but it doesn't do anything further. i wanted to go a step beyond that, so i built a tool that approximates ∂[prediction]/∂[feature], basically how sensitive the model prediction is to each feature of a given instance, allowing for effective risk management in areas where knowing how to change a prediction is more important than understanding the prediction itself. it's meant to be used for continuous and nondifferentiable black box models, especially ones like random forest or xgb. it uses a perturbation-based approach (heavily inspired by LIME, i really like that tool), where it pertubs each feature within a given window of the instance (window size controlled by feature distribution), and then computes secant slopes ( (f(perturbation) - f(original)) / (perturbation-original) ) for each perturbation and uses a linear regression (x=perturbation, y=secant slope) to estimate slope at original instance. secant slopes are gaussian weighted based on the perturbation's distance from original value. to be honest, the results were a little underwhelming. i compared my tool to simply using centered finite differences ( (f(x+h)-f(x-h)) / 2h where h is small ), and found that its performance was marginal on a pytorch nn (using autograd for ground truth). however, on a random forest model where gradients couldn't be analytically found, my tool's sensitivties remained much more stable compared to CFD, whose sensitivities depended heavily on size of the epsilon (the h-value). if you wanted to try it out it's pip install sage-explainer. more info on my github repo yashkher-123/sage. submitted by /u/Upstairs-Cup182 [link] [comments]

Why doesn't Anthropic add a tree view to Claude.ai?

So I've been using Claude pretty heavily for research and deep technical discussions, and one thing that drives me absolutely insane is the lack of a conversation tree view. For those who don't know what I mean: when you edit a message or start a new branch in a conversation, Claude actually does support branching under the hood. You can go back, edit a prompt, and get a different response, which creates a fork. But the UI just... hides all of this from you. You get a flat, linear chat with little arrows to navigate between versions Meanwhile there are third party tools and visualizers that take your Claude conversation data and render it as a proper node graph, showing you every branch, every fork, every parallel thread of reasoning. It looks incredible and is actually useful for research workflows. But here's the catch: it only works as a Chrome extension, so if you're on Firefox, Safari, or mobile, you're completely out of luck. And since it's a third party tool hooking into Claude's UI, every time Anthropic pushes a frontend update it has a good chance of just breaking entirely until the extension dev gets around to patching it. That's not a stable workflow anyone should have to depend on. This exact functionality should just be in the app. https://preview.redd.it/mzqcaii80s1h1.png?width=1488&format=png&auto=webp&s=5269aa4c52d3c7569fdfdf9e12c90791d06fc6e0 submitted by /u/Ok-Owl-5740 [link] [comments]

Started Learning - DL, feels stuck need help!

I recently started learning about the basics of deep learning using just Youtube videos and gemini or claude to explain about things when i get stuck. I started with - Yann Lecunn's course, where he recommended 3blue1brown for linear algebra. 3blue1brown is a youtube channel known for explaining math with matching visuals, I really loved the way linear algebra was explained there. I saw another playlist there for DL so i started that, where a book written by Michael Nielsen called "Neural Networks and Deep Learning: Introduction to the core principles." was recommended. Then i started reading this book, its been three days - hardly finished the first chapter (lot of math), feels like i'm stuck in a rabbit hole. I'm very curious to know how it all works, but it feels really overwhelming. Am i going the right way? submitted by /u/SensitiveDatabase102 [link] [comments]

the gap between chatgpt drafting an email and chatgpt actually sending it is wider than i expected

I spent the last few weeks trying to push chatgpt past "give me text" into actually finishing a workflow end to end: read the gmail thread, pull the matching hubspot record, draft the follow-up, file the next step in linear. it can describe each step beautifully. doing them in one pass without me copy-pasting between five tabs is a different problem. codex extending to mobile feels like openai noticing the same gap from the dev side. but for non-coders the gap is just as wide. an agent that actually does the work needs gmail, calendar, drive, a crm, and some cross-session memory of what happened last tuesday, and the moment you wire all of that up you are basically building a desktop app on top of the api. my guess is openai eats this from the inside (operator plus actions plus connectors plus memory) and third parties stitching things together get squeezed once the connectors mature. open question for me is whether that happens this year or three years out. submitted by /u/Deep_Ad1959 [link] [comments]

Guide or opinion on making mini textbooks for personal use?

Just got home after my last final at college and I’m wanting to get ahead of next semester by creating my own personal (non commercial) textbooks for subjects such as calculus 3-4, honors data structures, linear algebra, etc fit with my own preferences, teaching style, and imbedded practice tests/exercises. And potentially even something for teaching unity game engine. Any tips or ideas on how to make these high quality and useful? For example, claude loves making fancy HTML, but I don’t know if that is economical for tokens in the long run or I myself have never used the skills feature so that might be a start or not submitted by /u/Murky_Pilot_4627 [link] [comments]