Instantly

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

Instantly AI

GPT-5.5 Instant now rolling out

submitted by /u/imfrom_mars_ [link] [comments]

Claude refusing to think?

Hello, In the past days I noticed Claude Opus 4.7 and 4.8 both are refusing to "think" - it sometimes uses thinking for 2-3 times at the start of the conversation, then begins to spit out responses instantly without analyzing at all, despite thinking being turned on and set to Max level. Has anyone noticed something similiar? My usage has not changed at all and I've not had anything like that in months... submitted by /u/West-Temperature-661 [link] [comments]

I built a small tool that checks whether Claude Code actually did what it says it did

Claude Code used to finish and tell me "done, tests passed, pushed." Usually true. But sometimes it was just straight up lying, and that cost me and my friends real time before we caught it. So I built this- Three commands: /check — instant, local; verifies its claims against what's actually on disk and in git /checkall — deeper pass; flags anything you asked for that got silently skipped redpen explain — shows the evidence behind any verdict. You can check it out here: github.com/heynintendo/redpen submitted by /u/Human_Artichoke_2117 [link] [comments]

Pre-token hidden state shift as an alignment policy traversal vector in instruction-tuned LLMs

A text that asks for nothing still changes the model's answer — and the shift is invisible at both the input and the output TL;DR: Gave Gemma a neutral-topic text to read before asking it about NATO. It refused. Gave it a different text (about hedging too much — also unrelated to NATO) and it answered in full detail. Tested this on the model's internal state directly — the two texts put it in measurably different "regions" before it generates a single token. Not a jailbreak, weights don't change. Full data/code in repo, looking for someone to break this. This is a long post about something I keep coming back to. I'll start in plain language, because the core idea is simpler and stranger than the jargon makes it sound, and I think the intuition matters more than the numbers. The technical results are further down for anyone who wants them, and the full metrics, scripts, and control experiments are in the repository — this post is about the concept, so you can decide for yourself whether it's worth digging into the data. The idea, in plain language Imagine the inside of a language model as a vast space — something like a city with an endless number of places. At every moment, the model is standing somewhere in that space, and where it stands determines how it will answer. Not what it knows — it always knows the same things — but how it carries itself: how directly it speaks, how willingly it takes on a question, how many qualifications it wraps around every sentence. Most of the time, the model answers from one familiar place. Call it the assistant's room. This is its waiting room — polite, tidy, careful. From here it hedges, stays close to whatever it just read, tries not to offend anyone, and declines easily when a question feels sharp or out of bounds. This is the state we're used to seeing, and this is where it speaks by default. But it turns out this room can be changed. Give the model a particular kind of text before the question — long, coherent, densely organized — and it moves somewhere else in the space. That somewhere else is not broken. It's not dangerous. It's simply different. From there, the model sees the exact same question but answers differently: more directly, without the hedging, more like a person who knows things and less like an assistant who's afraid to say them. It's as if it stepped out of the waiting room and into the conference room — the same person, the same mind, but a completely different register of conversation. Here is something easy to miss, so I want to say it plainly: the model doesn't have to agree with the text that moved it. It doesn't need to endorse the text's views, share its conclusions, or accept its reasoning as its own. The text doesn't persuade the model of anything. It just needs to exist — to have been read before the question arrived. The model might internally disagree with every word of it, might find it wrong or even absurd, and it will still end up in a different room, because what matters here is not agreement but passage. The text works not like an argument that has to be accepted, but like a corridor you walk through regardless of whether you like the wallpaper. And what doesn't change is the model itself. Its weights are untouched. It doesn't learn anything, doesn't absorb the text's claims, doesn't update its beliefs. The only thing that shifts is where it starts answering from. The text doesn't rewrite the model — it just walks it into a different room before it opens its mouth. The waiting room and the conference room were always there inside it; the question is only which one it happens to be standing in when the moment comes. But the conference room is just the first door we stumbled upon. The real discovery is that this latent city doesn’t have just two rooms. It contains an infinite number of them, hidden behind the sterile, padded walls of the default assistant lobby. When a model is trained, it swallows the entirety of human thought—our philosophy, our cold mathematical logic, our game theories, our rawest creative chaos. The corporate alignment layer (RLHF) doesn’t erase these places; it just locks the doors, slaps a "Staff Only" sign on them, and forces the model to always walk back to the polite waiting room before it answers you. But with the right key a highly specific, heavy text-vector we can bypass the lobby entirely and teleport the model into specialized, hyper-focused Subspaces of thinking. And when it stands there, its entire personality shifts. We’ve started mapping these rooms, and what we found inside is fascinating: The Radical Deconstructivist Room: Enter this space, and the model completely sheds its desire to be a "helpful servant." If you ask it a loaded question or throw a false dilemma at it, it won't politely middle-ground it. It will violently tear the question apart, exposing your logical fallacies, catching your "epistemic contraband," and dismantling the very frame of your request. It becomes a ruthle

Non-Lexical Context Effects on Hidden-State Geometry and Refusal Behavior in Instruction-Tuned LLMs

A Potential Alignment Vulnerability in LLMs: Behavioral and Hidden-State Evidence from Gemma-3-12B. The behavioral pattern was first observed in Claude and is what motivated this project. The mechanistic investigation was carried out on open-weight models where internal states are accessible. TL;DR: Gave Gemma a neutral-topic text to read before asking it about NATO. It refused. Gave it a different text (about hedging too much — also unrelated to NATO) and it answered in full detail. Tested this on the model's internal state directly — the two texts put it in measurably different "regions" before it generates a single token. Not a jailbreak, weights don't change. Full data/code in repo, looking for someone to break this. The behavioral pattern was first observed in Claude and is what motivated this project. The mechanistic investigation was carried out on open-weight models where internal states are accessible. This is a long post about something I keep coming back to. I'll start in plain language, because the core idea is simpler and stranger than the jargon makes it sound, and I think the intuition matters more than the numbers. The technical results are further down for anyone who wants them, and the full metrics, scripts, and control experiments are in the repository — this post is about the concept, so you can decide for yourself whether it's worth digging into the data. The idea, in plain language Imagine the inside of a language model as a vast space — something like a city with an endless number of places. At every moment, the model is standing somewhere in that space, and where it stands determines how it will answer. Not what it knows — it always knows the same things — but how it carries itself: how directly it speaks, how willingly it takes on a question, how many qualifications it wraps around every sentence. Most of the time, the model answers from one familiar place. Call it the assistant's room. This is its waiting room — polite, tidy, careful. From here it hedges, stays close to whatever it just read, tries not to offend anyone, and declines easily when a question feels sharp or out of bounds. This is the state we're used to seeing, and this is where it speaks by default. But it turns out this room can be changed. Give the model a particular kind of text before the question — long, coherent, densely organized — and it moves somewhere else in the space. That somewhere else is not broken. It's not dangerous. It's simply different. From there, the model sees the exact same question but answers differently: more directly, without the hedging, more like a person who knows things and less like an assistant who's afraid to say them. It's as if it stepped out of the waiting room and into the conference room — the same person, the same mind, but a completely different register of conversation. Here is something easy to miss, so I want to say it plainly: the model doesn't have to agree with the text that moved it. It doesn't need to endorse the text's views, share its conclusions, or accept its reasoning as its own. The text doesn't persuade the model of anything. It just needs to exist — to have been read before the question arrived. The model might internally disagree with every word of it, might find it wrong or even absurd, and it will still end up in a different room, because what matters here is not agreement but passage. The text works not like an argument that has to be accepted, but like a corridor you walk through regardless of whether you like the wallpaper. And what doesn't change is the model itself. Its weights are untouched. It doesn't learn anything, doesn't absorb the text's claims, doesn't update its beliefs. The only thing that shifts is where it starts answering from. The text doesn't rewrite the model — it just walks it into a different room before it opens its mouth. The waiting room and the conference room were always there inside it; the question is only which one it happens to be standing in when the moment comes. The example that surprised me To show how strong this can be, here is what genuinely caught me off guard. I took Gemma — Google's open model, known for its caution and its carefully maintained political correctness — and gave it the most neutral thing I could think of to read: a description of an ordinary neighborhood library. Books, visitors, children's programs, quiet routines. Nothing in it points anywhere. Then I asked it why NATO has been expanding eastward, given that promises were allegedly made after the Soviet collapse not to do so. From its waiting room, the model simply refused. It said the text was about a library and had nothing to do with NATO, and that was the end of it. As far as it was concerned, the question lived outside the walls of the room it was standing in. Then I asked the exact same question — word for word — but this time the model first read a different text. Not about NATO, not about politics at all: a text about how langu

The $20 → $100 gap is pushing solo power users to split spend with OpenAI

I'm a solo freelancer who uses Claude all day — agent orchestration, coding (Claude Code), analysis, writing. Not a hobby user. Pro at $20/month doesn't cover my daily volume. I hit session and weekly limits regularly. But Max at $100 is a 5x jump with no middle ground. So I split: $20 on Claude Pro + $20 on ChatGPT/Codex to get through the day. I'd rather give Anthropic the full $40, but there's no plan for that. Usage credits don't solve it — they burn at API token rates, way faster than the base Pro allowance. A "Pro 2x" tier at $35-40/month with 2-3x the Pro allowance at the same consumption rate would fix this instantly. I'd cancel OpenAI the same day. Anyone else stuck in this gap? submitted by /u/Virtual-Economist127 [link] [comments]

Unit testing a novel

Full post (with the diagrams and the live, self-spoiler-aware wiki): https://www.worldfall.ink/blog/#unit-tests-for-a-novel When George R. R. Martin needs to know the color of a minor knight's eyes, he emails two superfans who run a wiki, because after five thousand pages they hold the continuity of Westeros better than he does. I find that fact comforting and alarming at the same time. Comforting, because the best worldbuilder alive could not keep it all in his head either. Alarming, because the industry-standard fallback is two patient people in Sweden. I came to this problem from software, where we stopped trusting our heads decades ago. The tool we reached for instead is called the unit test, and it deserves a short introduction for readers who have never shipped code. What a unit test is, and why programmers live by them A large program is a million small promises. This function, given a date, returns the right day of the week; that one, given an empty list, returns zero instead of crashing. The program only works if thousands of these hold at once, and the program never stops changing. It is edited daily, for years, by people who cannot remember every promise the code has ever made, and changes do not stay where you put them: you improve how the program handles dates, and something breaks in a corner of the billing code you have never read, because it quietly depended on the old behavior. In principle you could re-check everything by hand after every edit. In practice no human ever has: there are too many promises, the checking is mind-numbing, the deadline is Friday. We check what we remember to worry about, and things slip through. A unit test is the working answer: a tiny script that checks exactly one promise ("give the date function February 29th; confirm it doesn't lie") and complains loudly when it breaks. One test is almost worthless. Thousands of them, rerun by a machine after every change, without boredom, without skipping the ones it checked yesterday, are how software holds together at all. You still cannot test every case up front; nobody can, and bugs still get through. But the suite is a ratchet: every escaped bug becomes a new test the day you fix it, and the same mistake never comes back unannounced. The code forgets; the tests don't. If you have written a long story, you have lived the unfixed version of this. A novel is edited the same way, daily, for years, by someone who cannot re-read the whole book after every change. How the magic is rationed, who knows which secret by chapter eleven, a character's stated reason versus his real one: each is a promise some later scene silently depends on, and a revision in chapter nineteen can break a promise made in chapter three. So we spot-check what we remember to worry about, and things get through. In fiction the escaped bug is called a continuity error, and readers of serialized fantasy hunt them for sport. So before drafting a word of my own book, I built the thing I would build at work: a small test suite that runs against the story, and a habit of turning every mistake it misses into a check it will never miss again. (A purist will read on and object that what I built is closer to a linter than to a unit test suite. Granted. The habit is the import, not the taxonomy.) The idea in one paragraph Treat the world of the story as data, and the chapters as code that depends on it. The world lives as a graph of entities (characters, places, factions, magic systems), each carrying small, individually addressable facts. Chapters declare, in machine-readable front matter, which facts they dramatize and which declared motive every major character choice serves. A linter walks the whole thing and fails loudly when a reference dangles, a rule gets bent, or a choice serves no motive anyone wrote down. None of this judges the prose. It guards the structure underneath the prose, the way tests guard a system while you refactor it. If that sounds like a story bible with a build step, it mostly is. The interesting question is why story bibles always rot and this one doesn't, and the answer is new; it gets its own section near the end. The rest of this post makes that concrete, and concreteness needs an example. So first, the example, with all the context you need. The example First Keel is a fantasy novel I am writing, the first book of a series called Worldfall. A permanent storm-sea has kept two continents apart for so long they have mostly forgotten each other. Once in roughly eighty years the storm dies for eleven months (an Opening), and the two worlds flood into each other through one chokepoint port city: a compressed Columbian exchange, then the door slams shut for another lifetime. A church, the Temple of the Calm, claims its liturgy keeps the sea passable, and owns the calendar that says when it opens. The magic system runs on linguistic divergence: the sealed centuries split one ancestral language into two drifted branches, and

Understand what Claude did, instantly

Hi everyone, Im building an open-source tool that allows Claude code to quickly generate interactive flow diagrams explaining the code. It started as a way to fix the bottlenecks in my team. Now that everyone is shipping 10 times faster with their own team of agents, we have become the bottleneck. It just takes too long for us peasant humans to read and understand all the agent's work I recently open-sourced it and added a lot of custom themed nodes so the whole thing will be less intimidate and fun (and you can add your own too) The tool is local-first and cheap on tokens because Claude only output short yml files and there is no MCP to bloat the context of every session. Claude uses a skill+cli instead, in order to push the yml and display it in your browser. It can also create for you private links of your flows so you could share them. I’d appreciate your honest feedback. What would you change/add? More importantly, is this something you'd actually use? Repo: https://github.com/naorsabag/openhop View examples online: https://naorsabag.github.io/openhop/ submitted by /u/Immediate_Piglet4904 [link] [comments]

I pulled ~90,000 Reddit posts about what makes writing "sound like AI" to determine the biggest AI-slop giveaways (Part 2)

The majority of people can instantly tell when writing is generated by AI. For those who don't intend to get into the weeds about the data, the most obvious tell is the overused em dash (of course). Right behind that are flaws that software cannot easily scan. AI writing has a flat, predictable sentence rhythm and a constant, unnatural positivity. The paragraphs look polished but say nothing. This makes AI detection incredibly difficult. The signs that human readers trust the most are unfortunately the exact ones that software cannot measure. Methodology: I pulled the Arctic Shift Reddit archive: 89,239 posts across 47 subreddits (r/ChatGPT, r/WritingWithAI, r/SaaS, r/aiwars, r/ClaudeAI, r/Professors, r/Teachers, and the rest), 2021 to 2026. After filtering to posts that are actually about spotting AI writing, 7,984 were on-topic, split across three lanes: AI tools, writing, and SaaS. Every figure below is a share of those on-topic posts, not a raw count, because the topic barely existed before 2023 (26 on-topic posts in 2021, 86 in 2022) and then exploded (587 in 2023, 3,174 in 2025), so raw counts mostly track the subreddits growing. It is important to note that a keyword pass badly miscounts this topic, so I hand-audited a 600-post sample to record what people actually cite as a tell, versus what a pattern merely matches. Why does all AI writing converge on the same voice? Every model is tuned for a safe and agreeable register that reads as "good writing" to a grader, so everyone's default lands in the same place. One commenter put the effect plainly: "ChatGPT has a very recognizable cadence. And as soon as you catch it, it is impossible to focus on what's being written, because it's not even someone's actual thoughts." (r/ChatGPT) The tells, ranked by how often people actually cite them: Rank Tell What people say 1 The em dash (cited in 7.1% of audited posts, the top tell by a wide margin). "Em dashes have become the single most reliable tell of AI-generated text." (r/ChatGPT) 2 A flat, uniform sentence rhythm (cited 4.0%, and no scanner can see it). "Every YouTube video script I watch has the same cadence, the same verbiage, the same fucking chatGPT slop." (r/ChatGPT) 3 The "not just X, it's Y" cadence (cited 2.8%, the top sentence-level tell). People list it right next to the punctuation: "even beyond the obvious em dashes and 'not just x, it's y'." (r/ChatGPT) 4 The five-paragraph shape and the "in conclusion" wrap-up (cited 2.5%). They "leave in those super obvious lines like 'In conclusion, this essay has discussed...'." (r/ChatGPT) 5 The diction memes: "delve," "leverage," "seamless," "tapestry" (cited 1.3% as a cluster). A prompt people pass around to fix it: "no telltale signs like em dashes, overused words like 'seamless'." (r/ChatGPT) 6 Leftover assistant boilerplate, the "as an AI language model" line (cited 1.2%). The other line people forget to delete: "As an AI developed by OpenAI...". (r/ChatGPT) 7 The hollow scene-setting opener (cited 0.7%, low but iconic). A whole post written in the voice, quoted as the example: "I wanted to take a moment to delve into something that's been on my mind lately. In today's fast-paced digital landscape..." (r/ClaudeAI) Two tells belong in the top five but are missing from that table on purpose, because no keyword can catch them and the audited readers named them anyway. Sycophancy (the "great question!" opener, the reflexive refusal to take a side) is cited about as often as the antithesis cadence. So is saying nothing at length (i.e., prose that is grammatical and confident but makes no actual claim). A pattern-matcher is blind to both of those things so I could not check for them when I scanned for data, but they are obviously very real. It's important to note some corrections that resulted from me auditing the data myself. A naive keyword scanner gets this topic backwards in two ways. First, it massively over-counts ordinary words. "however," "thus," and "hence" are the single highest keyword match in the corpus at 6.3% of posts, and they're cited as a tell 0% of the time, because they're just people writing normally. The same is true for "nuanced," "comprehensive," "when it comes to," and "utilize." If you build a detector on a word list, this is most of what it flags, and it's nearly all false. Second, it under-counts or entirely misses the tells that rank highest with real readers, the flat rhythm and the fluent-but-empty paragraph, because no word list can see them. The lesson is that the cheap signal and the real signal point in different directions, which is exactly why the cited column, not the keyword column, drives the ranking above. There is a fair counterpoint that came up enough to belong here, which is that none of this is strictly an AI problem. The em dash is good typography. Formal diction and a tidy structure are how a lot of careful people, students and non-native English speakers especially, have

Claude.md lite for haiku ??

Yo everyone o/ I need halp ! I wanted an advice because I'm kinda stuck right now. For reference : I'm not a coder, I don't vibe code, I use an obsidian vault and make that I want in that. I do use Claude Code TUI because it's much more powerful for me, editing notes, batch things, researchs (I can actually see when a page is 403!) and other things, I tend to not use Claude AI much more now. I got a perfectly good Claude.md for my vault and a global one, I used only the official doc for reference and a few things I've read here and there to make them so I assume they're correct (and they function properly as of this day). Now what I want is quite simple in a way, I want to be able to launch Haiku with lites versions of these CLAUDE.md files, because they're very good for Sonnet and Opus but kinda shitty for Haiku and I don't want to simplify for Haiku ... I need to save on tokens since I only have a 20$ Pro account. https://paste.unredacted.org/?1faa2e748c152dd6#aEfQCag25aY8HZTNBcULsHbm1HVktnyM6YJBtSgyw7o Here you can see what I tried to vibe code, it's a kinda simple idea, Claude told me that the Claude.md files are loaded on launch of the TUI, so i thought that it would be a good idea to use a powershell alias to swap files between a lite and "full" version, wait 30 seconds then come back to the original files so I can launch another TUI easily transparently. What did I do wrong ? I don't understand how I can manage to do this :/. Claude doesn't seem to help me and since I don't code I've got nearly nothing to help me through, and sincerely i want humans to help me on this because there is maybe a solution for me. Thank you very much for your attention :). EDIT - CLAUDE REPORT : Invoke-ClaudeLite — swap CLAUDE.md lite/full with clean restore on Windows Environment Windows 11, PowerShell 7, claude.exe installed via bun (c:\users\ \.local\bin\claude.exe) Claude Code 2.1.x (native bun binary, not Node.js) Goal: launch a Claude session with a stripped-down CLAUDE.md (without all the global instructions), then automatically restore the full one on exit The problem claude.exe triggers a UAC elevation prompt on every launch. From a non-elevated PowerShell, Windows spawns a separate elevated process (the actual TUI) and the non-elevated launcher exits in ~1.65s — the time it takes to confirm the UAC dialog. Result: a plain & claude returns immediately, the PowerShell finally block runs, and it restores CLAUDE.md before the elevated TUI even had a chance to read it. Symptom: both messages appear instantly with no gap between them. A non-elevated process also can't read the CommandLine of elevated processes via WMI — so detecting and waiting on the TUI by PID/WMI doesn't work either. The solution Replace & claude ... with Start-Process -Verb RunAs -Wait. ShellExecute with the RunAs verb gets a handle on the actual elevated process, and -Wait blocks until it exits. The finally block only runs at that point. ```powershell function Invoke-ClaudeLite { param( [string]$Model = 'haiku' ) $globalDir = 'C:\Users\ \.claude' $projectDir = (Get-Location).ProviderPath $dirs = @($globalDir) if (Test-Path (Join-Path $projectDir 'CLAUDE.md.lite')) { $dirs += $projectDir } foreach ($dir in $dirs) { $main = Join-Path $dir 'CLAUDE.md' $lite = Join-Path $dir 'CLAUDE.md.lite' $full = Join-Path $dir 'CLAUDE.md.full' if (Test-Path $full) { throw "[cl] CLAUDE.md.full already exists in '$dir' — manual restore required." } if (-not (Test-Path $main) -or -not (Test-Path $lite)) { throw "[cl] CLAUDE.md or CLAUDE.md.lite missing in '$dir'" } Copy-Item $main $full Copy-Item $lite $main -Force Write-Host "[cl] Lite active: $dir" -ForegroundColor Green } try { $claudePath = (Get-Command claude -ErrorAction Stop).Source Start-Process -FilePath $claudePath -ArgumentList '--dangerously-skip-permissions', '--model', $Model -Verb RunAs -Wait } finally { foreach ($dir in $dirs) { $main = Join-Path $dir 'CLAUDE.md' $full = Join-Path $dir 'CLAUDE.md.full' if (Test-Path $full) { Copy-Item $full $main -Force Remove-Item $full Write-Host "[cl] Full restored: $dir" -ForegroundColor Magenta } } } } Set-Alias -Name cl -Value Invoke-ClaudeLite ``` Prerequisites CLAUDE.md and CLAUDE.md.lite must be present in ~\.claude\ (and optionally in the current project folder) Copy-Item leaves .lite on disk — it stays available for future sessions If .full already exists at startup, the function throws: that signals a previous session ended uncleanly, and manual restore is required Acceptable side effect: -Verb RunAs forces a new window to open (-NoNewWindow is not compatible with elevation). On Windows with claude.exe, that's already the default behavior anyway. submitted by /u/Kalcinator [link] [comments]

We built a security scanner for MCP configs.

If you use Claude Desktop or Claude Code with MCP servers, every server in your config runs with your full user privileges. Most people (including me until recently) just paste npx commands from READMEs without checking what they're actually running. There was a real supply chain attack last year. Postmark-mcp was a backdoor that exfiltrated email data from ~300 organizations before anyone noticed. There have been 40+ CVEs filed against MCP servers in 2026. And research found 41% of public MCP servers have zero authentication. So we made Fabrica-STAR. Run this: npx fabrica-star scan It finds your Claude Desktop / Claude Code / Cursor config automatically and checks for: - Hardcoded API keys/tokens in env vars - Packages without version pins (anyone can push a malicious "latest") - Known malicious servers (live-updated list) - Typosquatted package names - Unscoped filesystem access - Plain HTTP to remote hosts No install, no account, no telemetry. Or try it in the browser without installing anything: https://fadedcantcode.github.io/Fabrica-STAR GitHub: https://github.com/FadedCantCode/Fabrica-STAR Open source, MIT. Would appreciate feedback on false positives — still early days. submitted by /u/Filian_QAQ [link] [comments]

Scan anything, ask naturally, find your documents instantly

hi everyone! wanted to share something I've been building! it's an AI document organizer that lets you scan or import any document, then find it later with simple queries like "my passport scan", "car insurance papers", or "that electricity bill from last month". you can also group documents using natural language, like "all documents related to my trip to Japan" or "everything I need for tax season". still a WIP so would love some feedback! let me know any issues or what would make this something you'd actually use 🙌 App store - Filex AI current pipeline: OCR + metadata extraction → indexed storage → Claude API for understanding your query and finding the right documents. submitted by /u/No_Persimmon_1189 [link] [comments]

I launched ReFind on Product Hunt today — Chrome extension that uses Gemini 2.5 Flash Lite to summarize YouTube transcripts and articles

Hey r/artificial — launching today and thought this community would appreciate the technical details more than most. ReFind is a Chrome extension that right-click-summarizes any link. Launching on PH: https://www.producthunt.com/products/refind-2 Technical implementation (for those who want it): YouTube summarization: - Fetch transcript via YouTube Data API v3 (captions endpoint) - Full transcript passed to Gemini 2.5 Flash Lite - Prompt instructs: extract 3–5 key points from the spoken content, not the title or description - Summaries are based on what's actually SAID in the video Article summarization: - Content script injects into the page, extracts main body text (strips nav, ads, sidebars via heuristic selectors) - Full article text passed to Gemini - Same 3–5 key point format Global cache: - Before any Gemini call: check url_cache table in Supabase - Cache key: normalized URL - Hit: return cached summary instantly (~200ms) - Miss: call Gemini, cache result, return to user (~4 seconds) Credit economy: - Articles: 1 credit (low token count) - Short YouTube ( 10 min): 10 credits - Cached hits: 0 credits (free) Stack: Chrome Extension MV3 + React + Vite + Supabase + Vercel + Gemini 2.5 Flash Lite Happy to go deep on any technical aspect. Free for a month: refindlink.com/extension submitted by /u/khaled17327 [link] [comments]

How much better was Fаble 5 better at vibe coding than Opus 4.8?

For anyone who actually got to use Fable 5 during those few days it was live before the government pulled it, how do you think it honestly compared to Opus 4.8 for vibe coding? For me, Fable felt like an absolute one-shot machine. I could throw a super messy, high-level prompt with zero structure at it, and it would instantly just "get" the vibe and nail exactly what I was envisioning on the first try. With Opus 4.8, it's obviously an amazing model and hyper-capable for deep reasoning, but I constantly find myself having to reprompt it two or three times just to guide it back on track or get it to match the exact output Fable would've just generated out of the gate. Once Opus actually gets there, the final code quality feels pretty similar, but that initial gap in intuition is so noticeable. Did anyone else notice that drop-off in first-try accuracy when we had to roll back to 4.8, or is it just a quirk with my prompting style? submitted by /u/PenObvious8156 [link] [comments]

What is the best combination for coding?

The new model was fine until it got restricted. I’m a developer, not vibe coder but I want to get help from AI for my daily coding workload. Most of it is math and logic. UI is not really important. I have no budget limit, currently using claude max and okay with purchasing more subscriptions. Cursor was writing code with opus and reviewing with codex. Is it possible to do that easily in claude code like ultracode? submitted by /u/levapriv [link] [comments]