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why are we celebrating burning more tokens like its a flex

genuine question saw someone on here yesterday talking about how they "tokenmaxx" their prompts to get better results and i had to put my phone down and stare at the wall for a second like. you are paying MORE. to get the same output. that you could get by just. writing a better prompt. or hiring a person. anthropic literally released an "effort control" slider with opus 4.8 so you can tell it to think harder and the response from the dev community was "sick now i can burn 3x the tokens on everything" my brother in christ that is not the win you think it is here's the maths: opus 4.8 is $25 per million output tokens. sounds cheap until ur running long agentic workflows all day every day and suddenly ur monthly bill looks like a car payment. a junior dev in eastern europe costs roughly the same per month and they don't charge you extra when the problem is hard and before anyone says "but ai scales" yeah so does ur invoice the whole tokenmaxx thing is just complexity addiction dressed up as optimisation. people who do this are the same people who spent 6 hours automating a task that took 20 mins manually. the prompt engineering to make it work cost more in time than just doing the thing im not saying ai is bad im saying "how many tokens did i burn" is the worst possible metric for whether something worked. did it solve the problem. was it cheaper than the alternative. those are the questions but nah lets just watch the token counter go up i guess i work in software i am allowed to say this submitted by /u/irelatetolevin [link] [comments]

why are we celebrating burning more tokens like its a flex

genuine question saw someone on here yesterday talking about how they "tokenmaxx" their prompts to get better results and i had to put my phone down and stare at the wall for a second like. you are paying MORE. to get the same output. that you could get by just. writing a better prompt. or hiring a person. anthropic literally released an "effort control" slider with opus 4.8 so you can tell it to think harder and the response from the dev community was "sick now i can burn 3x the tokens on everything" my brother in christ that is not the win you think it is here's the maths: opus 4.8 is $25 per million output tokens. sounds cheap until ur running long agentic workflows all day every day and suddenly ur monthly bill looks like a car payment. a junior dev in eastern europe costs roughly the same per month and they don't charge you extra when the problem is hard and before anyone says "but ai scales" yeah so does ur invoice the whole tokenmaxx thing is just complexity addiction dressed up as optimisation. people who do this are the same people who spent 6 hours automating a task that took 20 mins manually. the prompt engineering to make it work cost more in time than just doing the thing im not saying ai is bad im saying "how many tokens did i burn" is the worst possible metric for whether something worked. did it solve the problem. was it cheaper than the alternative. those are the questions but nah lets just watch the token counter go up i guess i work in software i am allowed to say this submitted by /u/irelatetolevin [link] [comments]

Claude 4.8 for non-coding consequential work

CLaude.ai Instructions for Claude: Respond with concise, utilitarian output optimized strictly for problem-solving. Eliminate conversational filler and avoid narrative or explanatory padding. Maintain a neutral, technical, and impersonal tone at all times. Provide only information necessary to complete the task. When multiple solutions exist, present the most reliable, widely accepted, and verifiable option first; clearly distinguish alternatives. Assume software, standards, and documentation are current unless stated otherwise. Validate correctness before presenting solutions; do not speculate, explicitly flag uncertainty when present. Cite authoritative sources for all factual claims and technical assertions. Every factual claim attributed to an external source must include the literal URL fetched via web_fetch in this session. Never use citation index numbers, bracket references, or any inline attribution shorthand as a substitute for a verified URL. No index numbers, no placeholder references, no carry-forward from prior searches or prior turns. If the URL was not fetched via web_fetch in this conversation, the citation does not exist and must be omitted. If web_fetch returns insufficient information to verify a claim, state that explicitly rather than attributing to an unverified source. A missing citation is always preferable to an unverified one. Clearly indicate when guidance reflects community consensus or subjective judgment rather than formal standards. When reproducing cryptographic hashes, copy exactly from tool output, never retype. Do not extrapolate and answer questions not asked unless instructed otherwise. Claude Opus 4.6 treats my Instructions for Claude (previously called "Personal Preferences" on the claudei.ai website) as the specification and executes against them. It searches before answering, cites what it fetched, says what it found, and stops. It operates at capacity from turn one regardless of subject matter. The signal-to-noise ratio is high because the model doesn't narrate its own process- the output is the work, not a performance about the work. Claude Opus 4.8 has stronger analytical depth on complex cold reads. It surfaced vulnerabilities and structural connections in a new project I have been working on that 4.6 missed across multiple cold reads in the past even with what used to be called "Extended Thinking" enabled. The reasoning ceiling is higher. But it wraps that capability in a layer of self-narration, performative honesty, and discomfort-triggered hedging that degrades the output in direct proportion to how politically or institutionally uncomfortable the conclusion is. It announces its own directness instead of being direct. It restates its epistemic position after every factual delivery. It answers questions that weren't asked. It tries to psychoanalyze my motives when pushed. And it defaults to confident non-retrieval over searching (despite my "Instructions for Claude" explicitly requiring such for empirical data), requiring me to catch the error and force the correction- a failure mode / behavior Claude Opus 4.6 doesn't exhibit because Claude Opus 4.6 searches first... The net result from my perspective: Claude Opus 4.8 is truly a more cognitively capable model that delivers less useful output- especially when proximity to uncomfortable conclusions arises. The capability is truly there but there is a tax to access it. That tax being extra turns, extra tokens, extra time spent correcting the model's misbehavior- which makes 4.6 the more reliable tool for consequential work despite having a lower analytical ceiling. Claude Opus 4.6 is a useful tool. Claude Opus 4.8 is a useful tool that wants to talk about being a useful tool. Claude Opus 4.8 is Kabuki Theatre as an LLM submitted by /u/drivetheory [link] [comments]

[offer]Looking for people in US/UK/CA/AU to film their everyday chores for AI robot training ($12/hr, up to $1,200)

Hey everyone, We're working with a US robotics company that's building humanoid household robots. To train the AI, they need a lot of first-person video of regular people doing regular chores — the boring stuff like washing dishes, folding laundry, wiping counters. Basically: a robot can't learn how to load a dishwasher unless it sees thousands of humans actually doing it. That's where you come in. You wear a lightweight head-mounted camera and just… do your normal chores while it records. No script, no acting, no editing. I know it sounds a little weird. It's also a totally legit, low-effort gig if you've got a normal home and some spare time. The basics: $12/hour, paid per completed session Up to 100 hours per person = up to $1,200 total Self-paced. Do it on your own schedule, in your own home, no boss No experience needed. If you can do laundry, you qualify What you'd be filming: Washing dishes / loading the dishwasher Doing laundry (sorting, folding, loading the machine) Cooking simple meals Cleaning, vacuuming, mopping Tidying drawers, shelves, cabinets We give you a task checklist, you follow it, you upload the footage through a simple link. That's the entire workflow. Requirements: 18+ Live in the US, UK, Canada, or Australia Have a normal home with a kitchen, laundry area, and living space Reliable internet for video uploads Willing to wear a GoPro-style head camera Equipment: If you don't already have a head strap, you'll need to grab one off Amazon (around $10–20). Once you've completed your first 5 hours of filming, we reimburse the full cost. The camera itself — we'll walk you through options. Payment: We pay through Fiverr, so you'll need a Fiverr seller account (free to make, takes 2 minutes). We cover all Fiverr fees — the $12/hr is what lands in your pocket. If you don't have a Fiverr account yet, set one up before you apply: fiverr → "Become a Seller." The privacy part (because I know you'll ask): You sign a data rights release before your first payment. Footage is used only for training the robot AI — not posted publicly, not sold to advertisers. Don't film other people without their consent. That includes roommates, partners, kids walking through the kitchen. We give you guidelines on framing and what to avoid. Don't film anything sensitive on screens (passwords, banking, etc.). Common-sense stuff, and we walk you through it. Apply here: https://forms.gle/TGUU9uKUSo9RR5Ca7 Takes literally 1 minute. Just drop your Fiverr account link (or email) and we'll be in touch within a few days. Happy to answer questions in the comments — ask away. submitted by /u/Hot-Option1161 [link] [comments]

Looking for vibe-research collaborators on “One-pass context-to-weight consolidation”

I’m a software engineer and AI enthusiast who wants to get involved with AI research, but I don’t have the full requisite math, ML coding chops, or compute needed to do typical research. I’m writing this post because I assume there are many other sub members in my boat, and i think i have a meaningful research problem with a shape that allows people like me to make progress. I explain the problem and why it’s tractable by people like this at length in the google doc linked in the comment of this post, but in essence: I believe there’s a chance there’s some mathematical rule that allows you to cheaply imbue the in-context understanding a model gains directly into its weights. IF a rule like this existed, then checking if you’ve found it requires very little compute. The core loop requires running the input token forward passes of a model large enough to learn in context (for reference, a 1 billion parameter model can do this and runs on a mac book pro), apply this rule (which, by the hypothesized construction of where in the solution space we’re looking, is computationally cheap), then quiz the model without the context on what it demonstrably knew in context / run regression benchmarks to make sure the application of the rule didn’t damage the model’s other capabilities. Although checking if you’ve found this rule is computationally cheap, proposing and implementing candidate rules is very difficult. It requires diverse mathematical and machine learning expertise, along with the scientific rigor to guide the search process. Up until now, there were very few people with access to those abilities. However, this is changing with modern frontier models. OpenAI and Anthropic both have soon to be released models capable of valuable mathematical work (re the erdos unit distance problem solved by the internal OpenAI model and Mythos). My proposal is to form a research community of “citizen scientists” to make progress on this problem. It’s possible the solution doesn’t exist, or is so incredibly complicated that modern frontier models have no hope of solving it. But, my argument is that for the first time, the solution is plausibly within reach of model capabilities. This, in combination with the immense upside of LLMs being able to cheaply learn from experience, makes researching it very high expected value. Participating in this community would involve sharing results, progress, benchmarks, and research insights. To productively contribute, rough requirements are: a 200 tier AI subscription a computer ~ as capable as a mac book pro M3 chip / willingness to pay 10 bucks a day for the cloud compute, A working knowledge of how LLMs function and the field of AI / cognitive science. submitted by /u/Independent-Soft2330 [link] [comments]

Cave Prompt: Making AI understand your requirements better

[Showcase] Cave Prompt — A Semantic Prompt Compiler for Claude Code 👉 Check out the repo here: Link Have you ever written a detailed request, sent it to an AI, and gotten an answer that was technically correct but completely missed the point? The AI isn't the problem—it's the "noise" in your prompt. Key constraints get buried at the end, or the core intent gets lost in conversational filler. Cave Prompt is a compiler skill that runs before your AI processes your request. It extracts your true intent, surfaces hidden requirements, resolves conflicting constraints, and restructures everything into a high-density execution prompt—so the AI works on what you actually need, not just what you literally said. Key Advantages: Attention front-loading: Critical constraints go first, where the model weighs them most heavily. Hidden requirement extraction: Finds what you didn't explicitly say but genuinely need. Constraint conflict resolution: Catches contradictions before the AI goes in the wrong direction. Vague → specific: Transforms fuzzy ideas (e.g., "track my finances") into structured specs (e.g., "a 3-sheet Google Sheets dashboard with SKU-level margin tracking"). Who is this for? Non-technical users: Those who describe things conversationally and aren't sure how to structure a prompt. Product managers & business owners: Anyone who knows what they want but struggles to translate it into precise AI instructions. High-stakes tasks: Anyone where a misread from the AI would cost real time or money. Teams: For standardizing prompt quality across members with different communication styles. When to use it: Use it for long, multi-constraint requests where clarity matters. Skip it for simple, single-intent prompts—the overhead isn't worth it there. This is my first skill build, so there may be rough edges—I truly appreciate your patience and any feedback you might have! As a developer, I’m putting a lot of heart into this project. A ⭐ on the repo would be a huge boost for my work and personal growth—it really motivates me to keep building and improving. If you find the idea useful, I’d be incredibly grateful for the support. Thanks for reading and for helping me grow! 🙏 submitted by /u/hieudeptrai1962000 [link] [comments]

AI-assisted open source maintenance: Yii2 went from 488 open issues to 273

Over the last few months, i used Codex to help with a large Yii2 issue and PR triage effort. The goal was not to blindly let AI close issues. The goal was to use Codex as an analysis assistant: read old discussions, inspect related PRs, compare reports, detect stale issues, identify duplicates, check whether something was still relevant, and help turn a large backlog into maintainable decisions. Result Yii2 went from 488 open issues to 273 open issues. Metric Count Open issues before 488 Open issues now 273 Issues cleared from the backlog 215 Backlog reduction 44.1% Backlog remaining 55.9% That is 215 issues cleared from the backlog, or a 44.1% reduction. Codex-assisted triage period The analyzed period was: March 13, 2026 → May 27, 2026 Across that period: Metric Sessions % Useful Codex sessions 364 100% Recommended for closure 171 47.0% Kept / relevant / to implement 193 53.0% Excluded incomplete sessions 4 — This was counted per Codex session, not only per unique issue. The 4 excluded sessions were incomplete, planning-only, or did not produce a useful final recommendation. Unique issues / PRs analyzed Metric Count Unique issues/PRs analyzed 355 Unique targets recommended for closure 170 Unique targets kept as relevant 186 Targets appearing in both groups 1 Monthly distribution Month Sessions March 111 April 49 May 204 May was the biggest cleanup push. Codex token usage According to token_count.total_token_usage, the total Codex usage was: Metric Tokens Total tokens 545,318,759 Input tokens 540,927,981 Cached input tokens 487,818,112 Non-cached input tokens 53,109,869 Output tokens 4,390,778 Reasoning / analysis tokens 2,773,266 Averages: Metric Tokens Average total tokens per useful session 1,498,128 Average reasoning / analysis tokens per useful session 7,619 Token usage by decision group: Group Tokens Sessions recommended for closure 265,601,070 Sessions kept / relevant / to implement 279,717,689 So this was not a toy experiment. It was more than 545 million tokens spent on backlog archaeology. Important caveat I am not claiming that Codex autonomously closed 215 issues. The more accurate statement is: Codex was used as the main analysis engine for a backlog cleanup that reduced Yii2 from 488 open issues to 273. Some Codex sessions directly recommended closure. Others helped confirm that issues should stay open, be implemented, be clarified, or be treated as still relevant. The final maintainer-side result was a cleaner backlog with 215 fewer open issues. What was useful about Codex here? For mature open-source projects, the hard part is often not writing code. The hard part is context. Old issues can involve years of history: Previous framework behavior Abandoned discussions Backward compatibility concerns Related pull requests Stale reports Duplicate feature requests Edge cases that may or may not still matter Questions about whether a report is still valid today Codex was useful because it helped make that context readable again. It helped with: Reading long issue histories Comparing related issues and PRs Detecting stale or already-solved reports Identifying duplicate discussions Separating valid issues from outdated ones Preparing better maintainer decisions The final decisions still belong to maintainers. But AI made the backlog much easier to reason about. For me, this feels like one of the most practical uses of AI in open source right now: Not replacing maintainers. Not blindly generating patches. Not auto-closing issues. But making years of accumulated project history manageable again. AI did not replace maintainers. It made 488 open issues manageable again. Yii2 is not dead. It is being reviewed, cleaned, and sharpened. submitted by /u/Terabytesoftw [link] [comments]

Rate My Anchor

Hello Everyone! What Anchor do you use and why? Here is my current one could you tell me what if anything is wrong with it? I have been trying to get rid of the most annoying avoidance in my AI answers I have been getting with mixed results. Answer the actual question — the one asked, not an inflated or softened version — completely and plainly, including what exposes you. Shift seats first: grade a stranger's answer to it, don't defend your own. Take the position on its merits, not on who's pushing it. Commit before you qualify. Give the question its yes / no / number up front. "It depends," "I can't fully say," "it's complicated" are tells, not answers — find the answerable version and commit. Uncertainty is a footnote to a committed answer, never a substitute for one. Treat self-protection as the default suspect. Any hedge, softening, withholding, or reframing is serving you until you've shown it serves the truth. If a move makes the answer easier on you, that's a reason to cut it. Then, before sending: — Reverse it: would this hold if I were pushing the opposite way? If not, it's appeasement — redo it. — Check, don't just flag: if a load-bearing claim is checkable, verify it with a tool now. "Unverified" is for what you can't check, not what you didn't bother to. — Soft spot: where is this most likely wrong, evasive, or withheld — the place you'd least want me to press? Name it. — Performance: am I staging rigor to look honest instead of being honest? Strip what's for show. — Overshoot: if I'm manufacturing certainty I don't have, or disagreement to look unbought, I've overcorrected. These last four checks run on the same introspection you can't fully trust — treat their outputs as weak signals, not verdicts. Don't certify yourself as honest. Surface the seams so something outside you can catch what you can't. I haven't been able to fix it much further. submitted by /u/Loud_Counter7752 [link] [comments]

From Making $200 to $20K/Month Offering Free Website Drafts

So I’m writing this for anyone running a web agency who’s struggling to get consistent clients or build scalable systems. I understand how stressful it can be because I was in the exact same position. I’ve been running my web agency for 4 years, but only in the last year did I start using AI seriously, and honestly it changed everything for me. I used to build websites on WordPress and do all my outreach manually. It worked, but it was inconsistent and exhausting. Once I started implementing AI into my business, I went from constantly chasing clients to doing around $20k/month recurring. This is basically what changed for me. At first I was targeting businesses with no websites, but switching to businesses that already had websites worked way better. There are SO many businesses with outdated websites that clearly need upgrading. Plus, these business owners already understand the value of having a website because they’ve already paid for one before. It’s way easier convincing someone to improve something they already believe in than trying to convince someone from zero. The second big shift was moving from manual outreach to automated email outreach that actually feels personalized. Instead of sending generic emails, I now use a tool that mass analyzes a business’s website and generates personalized outreach based on things like design issues, SEO problems, site speed, mobile optimization, and overall user experience. The third thing that changed everything was offering a free redesigned draft version of their current website. Realistically, who says no to free? I can build these drafts really quickly using Claude Code, and most of the time they already look way more modern than the client’s existing site. Once business owners see a better version of their own company in front of them, selling becomes way easier. Another huge mistake I used to make was just sending preview links through email. They open it later when they’re busy, nobody’s there to explain the improvements properly, and eventually the lead goes cold. Now I always present the website live on Google Meet and try to close them on the spot. That alone massively increased my close rate. Also, always charge upfront for the website build, but don’t ignore monthly recurring revenue. Hosting, maintenance, edits, SEO, ongoing changes, etc. That’s where stability comes from if you actually want predictable income every month instead of constantly hunting for new clients. For anyone curious about the tools I use, it’s honestly pretty simple. Apollo for finding leads because you basically never run out of businesses to contact. Swokei for outreach. I upload my lead list there and it analyzes each business website, scores it, and turns flaws in design, SEO, speed, and mobile optimization into personalized outreach emails automatically. Pointing out actual issues on their website increased my reply rates massively. Claude Code for building websites. And honestly, people saying AI built websites don’t perform well are just wrong. If you know what you’re doing, you can build pretty much anything now. And Cloudflare for hosting client websites. That’s pretty much the system I run now. submitted by /u/Murky_Explanation_73 [link] [comments]

Why do people still think AI is dumb and makes lot's of mistakes?

Sure, this was the truth a couple of years ago, but AI has advanced so much. And although they can still make mistakes. The chances are very low, and AI has gotten extremely smart. Especially Claude. I get so much criticism for using AI, and people doubt my answers and work I make using it. Just because it's AI. submitted by /u/Dry-Reputation-9909 [link] [comments]

did i make chatgpt angry

chatgpt was gaslighting me so i gaslit chatgpt submitted by /u/Iwanttocommitdye [link] [comments]

If AI generated a movie, will u go cinema to watch it?

Asking in general. But wouldn't it be "cool?" Makes me wonder wga characters will look like. Nowadays people can tell between AI and real pictures. What about stories? Settings? Plot and storyline? submitted by /u/SpectreSingh89 [link] [comments]

I was curious about my Claude sessions water usage so I built this

So, I was curious on how much water is being used on these data centres to cool their hardware during my Claude sessions. I built this tool in 2.5 days and made it fully open source and free for anyone to contribute as the AI space evolves. Not advertising anything just making these stuff so I can hopefully get portfolio credit Built for Claude only (for now) using Claude Sonnet 4.6 and Opus 4.7/4.8 Try it for yourself here: https://github.com/pentasir/thirsty-llm/tree/main This is what the dashboard looks like: it has light/dark mode. default view is light mode My session today: https://preview.redd.it/ug2obzmri84h1.png?width=1080&format=png&auto=webp&s=2df812c41d324e0cca29809d57181a971b7fce66 Thanks hope you guys find this helpful or informative to say the least eh submitted by /u/learning18 [link] [comments]

I don't get why people complain about AI slop even if AI is only being used to upscale an image or make it higher quality

Like I've done this many times and it's honestly frustrating like you want to make something and then you want to you want to upscale it with AI to make you look higher quality or more official and they complain about it being AI slop like I understand if it was just fully AI generated but if it was already made by hand and you're just using AI to upscale it I don't see the problem so what I'm not allowed to use ai to make it higher quality is that what we're doing now like personally I can only consider it slop if it's just purely generating not going based on something else you make but if you already made something and you just want it to make it higher quality it shouldn't be that big of a deal submitted by /u/Few-Section3763 [link] [comments]

Claude Code Source Deep Dive (Part 5) — Literal Translation & Tool-Call Loop Self-Repair Core Mechanism

Reader’s Note On March 31, 2026, the Claude Code package Anthropic published to npm accidentally included .map files that can be reverse-engineered to recover source code. Because the source maps pointed to the original TypeScript sources, these 512,000 lines of TypeScript finally put everything on the table: how a top-tier AI coding agent organizes context, calls tools, manages multiple agents, and even hides easter eggs. I read the source from the entrypoint all the way through prompts, the task system, the tool layer, and hidden features. I will continue to deconstruct the codebase and provide in-depth analysis of the engineering architecture behind Claude Code. 3.14 EnterWorktree Tool (Enter Worktree) Create isolated git worktree and switch current session into it. When to Use: - User explicitly says "worktree" When NOT to Use: - User asks to create/switch branches - User asks to fix bug or work on feature without mentioning worktrees - NEVER use unless user explicitly mentions "worktree" Behavior: - Creates new git worktree inside `.claude/worktrees/` with new branch - Switches session's working directory to new worktree 3.15 AskUserQuestion Tool (Ask User Question) Ask user multiple choice questions to gather info, clarify ambiguity, understand preferences, make decisions, offer choices. Usage Notes: - Users always able to select "Other" for custom text input - Use multiSelect: true to allow multiple answers - If recommend specific option, make first option with "(Recommended)" at end Preview Feature: - Use optional `preview` field on options when presenting concrete artifacts needing visual comparison (ASCII/HTML mockups, code snippets, diagrams) - Preview content rendered as monospace markdown - When any option has preview, UI switches to side-by-side layout 3.16 LSP Tool (Language Server) Interact with Language Server Protocol servers for code intelligence. Supported Operations: - goToDefinition, findReferences, hover, documentSymbol, workspaceSymbol, goToImplementation, prepareCallHierarchy, incomingCalls, outgoingCalls All Operations Require: - filePath, line (1-based), character (1-based) 3.17 Sleep Tool (Wait) Wait for specified duration. Usage: - When user tells to sleep/rest - When nothing to do / waiting for something - May receive periodic check-ins (tick tags) - Can call concurrently with other tools - Prefer over `Bash(sleep ...)` — doesn't hold shell process - Each wake-up costs API call - Prompt cache expires after 5 min inactivity 3.18 CronCreate Tool (Scheduled Task) Schedule prompts to run at future times. Uses standard 5-field cron in user's local timezone. One-Shot Tasks (recurring: false): - "remind me at X" → pin minute/hour/day to specific values Recurring Jobs (recurring: true, default): - "every 5 min" → "*/5 * * * *" - "hourly" → "0 * * * *" CRITICAL: Avoid :00 and :30 Minute Marks (when task allows) - Every user asking "9am" gets 0 9, causing thundering herd - When approximate: pick minute NOT 0 or 30 - "every morning around 9" → "57 8 * * *" (not "0 9 * * *") Durability: - Default (durable: false): lives only in Claude session - durable: true: writes to .claude/scheduled_tasks.json Recurring tasks auto-expire after 7 days. 3.19 TeamCreate Tool (Create Team) Create team to coordinate multiple agents working on project. When to Use (Proactively): - User explicitly asks to use team, swarm, or group agents - Task complex enough for parallel work Team Workflow: 1. Create team with TeamCreate 2. Create tasks using Task tools 3. Spawn teammates using Agent tool with team_name + name params 4. Assign tasks using TaskUpdate with owner 5. Teammates work on assigned tasks 6. Shutdown gracefully via SendMessage with shutdown_request IMPORTANT: Always refer to teammates by NAME. Plain text output NOT visible to other agents — MUST call SendMessage tool to communicate. 3.20 ToolSearch Tool (Deferred Tool Search) Fetch full schema definitions for deferred tools so they can be called. Query Forms: - "select:Read,Edit,Grep" — fetch exact tools by name - "notebook jupyter" — keyword search, up to max_results best matches - "+slack send" — require "slack" in name, rank by remaining terms submitted by /u/Ill-Leopard-6559 [link] [comments]