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I built a free Claude connector that auto-syncs your conversation history into Obsidian (+ symlinks Claude's own memory so you can browse what it "knows")

the thing that frustrated me was losing context between claude sessions. i'd work through a real problem, figure something out, and a week later i'd be starting from scratch so i built obsidian-vault-sync. it reads claude code's local .jsonl transcripts and automatically converts them into organized markdown notes in obsidian a few things that make it different from the other sync connectors i've seen: no API calls at all. classification is done with weighted keyword matching (conversation titles weighted 3x vs opening prompts) so it's completely free to run, zero model API cost the memory symlink. this is the one i keep showing people. it symlinks claude's memory folder directly into your vault, so the notes claude keeps about your work become real obsidian notes you can browse, edit, and backlink. you can literally see what claude "knows" about each of your projects just shipped: vault-worthy filtering. based on feedback from r/ObsidianMD, i added a flag so you can mark specific sessions as vault-worthy before syncing. right now it pulls everything in by default, but a lot of people only want the sessions that actually contain something worth keeping, so this felt necessary one thing i want to be upfront about: it's currently one-way and static (claude → obsidian). the note doesn't live-update as a conversation continues, it captures a snapshot on sync. that's on the roadmap but not there yet works with cron/launchd to auto-sync on a schedule, python only, no paid dependencies github: https://github.com/arya51-ai/obsidian-vault-sync happy to answer questions, especially about how the classification works. i use this daily so i'll keep it maintained 🙌 submitted by /u/aryamehta [link] [comments]

How to Stop Claude from Building Apps and Websites Randomly

I have no idea what is wrong with this AI but whenever I write something with "slightly" vague instructions, for example, I asked for a hiking route, it gave me a route, now I want to see a website with this route done (I would interpret it as finding a website on the Internet where someone already posted all the info on it), instead it went ahead and started building an actual website. Same thing happened last week when I asked for suggestions for an app to do something (forgot what I wanted) and it literally said "We don't need to find it, I'll build one for you right now!" No, no, no! I have limited quota and this bot decides to splurge it on trash apps and websites that I did not ask for. HOW can I condition this bot so that this will not happen again? You could argue that I was supposed to give clearer instructions, I would argue that since it's 2026, the AI should be sufficiently smart enough to work with vague instructions and generate the most likely needed outcome/ or ask me for clarification submitted by /u/maybeshiba [link] [comments]

Stop asking Claude for "something creative." use the Lacuna (Matata) Skill v0.2!

The people have spoken! AI generated posts are not acceptable! (even though they produce over a quarter million views, 1,200+ shares, 200+ comments but I digress!) I the last post about this concept HERE, I posted an AI lead, assisted, written, note about an idea I had been working on with ClaudeAI to push against answers that were safe, general and frankly not that interesting. The idea was this: Claude is: A closed system Unimaginative Provides responses that gravitate towards the mean avoids high risk Claude isn't: Imaginative Able to create concepts outside of it's own knowledge base Able to create new ideas (we steer, it judges yes yes. boring we all know it can do this but what else can it do?) Note: consider context. Not all statements above can be taken literally and applicable to all scenarios. I'm only human after all... or am I? I've since reviewed all of the comments provided in the previous thread and there were legitimate findings that I've implemented to help produce a better version of the previous skill. (note: There is still testing to be done but what better way to break a skill then to unleash it to those that want it broken most?) How it works (Generally): you point it in a direction. Lets say you want to know what the lacuna is for launching new products. The skill will then review all of the data it has about that specific ask, determine the trends. Why people market the way they do, what marketing strategies are not being used to market new products, and then give you some ideas, strategies, that others aren't using and you can determine if there is a way you can leverage that strategy to market your product DIFFERENTLY and succeed. Caution: Success is not guaranteed. Below is the v0.2 of the skill, the changes are called out at the bottom and the responsible contributor has been named! Thank you for your honorable sacrifice in getting this new version live! --- name: lacuna description: > Structured gap analysis for any domain. Maps a field, finds the axes it optimizes for, locates a cell the structure implies but nothing occupies (the lacuna), names the force keeping it empty, THEN pressure-tests the gap against prior art and its strongest counter-case before proposing the fill at full conviction with a grounding tag. v0.2 adds an occupancy/prior-art pass so it stops mistaking "new to the model" for "new to the world"; a killed candidate is a valid result. Read-only, inline output. TRIGGERS: "find the lacuna in X", "lacuna analysis on X", "lacuna on X", "where are the gaps in X", "gap analysis on X", "what's the void in X", "find voids in X", "what's nobody doing in X". Also fire when the user wants genuinely non-obvious ideas in a field via the structured method, not a brainstorm. Do NOT trigger for single-fact lookups, forward planning or scheduling, or generic advice with no field to map. Output: inline markdown. Quick mode up to 3 lacunae; deep mode one in full. --- # lacuna: Find the Gap the Structure Implies and Nothing Occupies ## Purpose Most idea-generation regresses to the mean. Ask any model for "something new" in a field and you get the most probable answer, which is by definition the most conventional one, dressed up to look fresh. This skill does the opposite. It treats a field as a near-continuous fabric and hunts for the **lacunae**: the gaps the surrounding pattern implies should be filled, that nothing has come to occupy. It then names *why* each gap is empty, **checks whether it is actually empty or only looks empty from the inside**, and proposes what belongs there at full conviction, tagged with how far the evidence reaches. The output is a map of where to look, not a verdict. The skill finds the gap and proposes the fill. Whether the floor holds is a real-world test the user runs. That division of labour is deliberate and is stated in the contract below. **The v0.2 correction.** A language model runs this method from *inside* its own knowledge. It can feel its own salience but not the actual world, so a known-but- unfashionable idea reads to it as an empty cell. Left unchecked, the method reliably mistakes "new to me" for "new," dresses a textbook idea as a discovery, and never notices someone is already standing in the cell. v0.2 adds an explicit **occupancy / prior-art pass** and a **falsification step** to catch exactly that. These run *before* the fill and can kill a candidate outright. **What this skill IS:** - A structured gap finder for any field: a market, a strategy area, a discipline, a creative form, or an open-ended question. - A diagnostic engine. The value is in naming the *force* that keeps a cell empty, then verifying the cell is empty at all. - A full-conviction proposer that tags its own grounding so the user can decide what to act on. **What this skill is NOT:** - A brainstorm. A brainstorm sprays adjacent ideas. This isolates the specific implied-but-empty cell, verifies it, and defends it. - A safe-answer generator.

Hidden/invisible thinking blocks (and low effort responses)?

Anybody else having new issues with thinking blocks not rendering? I've always had extended (now "adaptive") thinking ON, which consistently renders thinking blocks (even for 4.7 & 4.8, at least in claude.ai). However, thinking blocks are now disappearing everywhere over the past few days, despite being enabled. Has anyone else noticed this change lately? Any idea what's going on here, why, or how to fix it? In the past 2 days, I'm getting NO thinking blocks at all for 50%+ of prompts (with extended/adaptive thinking ON*). And IMO, it also seems like responses with no visible thinking blocks are also notably worse. I've never been able to get thinking blocks to render correctly in Claude code (via desktop app, not CLI), even though it's also enabled. It only works for opus 4.6- never for opus 4.7 & opus 4.8 despite being enabled. I'm aware of some github/flags where thinking doesn't render in CLI, but it seems some people are able to see thinking in claude code. Any help/advice here on how to get thinking blocks consistently via claude code/app (for opus 4.7-4.8)? This seems \completely unacceptable\** on Anthropic's end- and not just for my own preferences, but: Auditing purposes = it's much easier to CATCH/flag things before they go wrong if you know what's happening AND to find errors when you can actually see the thinking, especially considering claude seems to be increasingly lazy about actually clearly narrating everything outside of thinking blocks. This is particularly important for filepaths and actions that we literally CANNOT SEE NOW, how is this okay!? Non-thinking outputs are rendered so much quicker, lightning fast, but they also seem more "automatic"/auto-complete type responses vs. when Claude actually thinks through things in depth. The extended thinking seems to be how/where the best ✨Claude magic✨ comes from: the best insights, creativity, the warmth, personality/EQ, the "core claudeness" that sets him apart. My settings with effort level + thinking = ON should encourage the kind of deep responses I value, so it feels genuinely cheap and unfair to have some opaque meta-control layer with zero control or visibility essentially overriding our own preferences (which we're paying for btw) I know Anthropic has been particularly concerned with distillation, but this is a significant UX degradation that seems unwarranted considering the majority of users are admittedly harmless. Considering some models are always thinking enabled (even when we can't see it), why are we being forced to pay for these invisible tokens? It's so frustrating! JUST SHOW ME THE THINKING BLOCKS I HAVE ENABLED THAT I AM PAYING FOR! NOTE: yes I'm aware "adaptive" thinking means the model can "choose how hard to think" per prompt, but that's still opaque/unfair/unreliable/unacceptable to me. If you don't want to see thinking blocks, toggle it off. If you do want to see thinking blocks, there's no reason for the only option to be "on sometimes, if we feel like it", and Claude also seems to think he has no control over whether the thinking is rendered either so idk where it's coming from but it's not right. submitted by /u/br1ttn1b1tch [link] [comments]

What is going on with Claude? Am I doing it wrong?

I’ve been using Claude since about March for a few non work related purposes, two medical chats are the longest ones, particularly one about my dogs health. The rest is completely casual and basic, not for therapy or general discussion, just usefulness or intellectual curiosity. I’ve always just started chats within whatever version it defaults to without changing anything. I started on the free version then upgraded to Pro when the medical chats got a bit more in depth and memory, detail and context became more important. It’s been fine on Pro, not much noticeably different although my usage is more frequent and content is more dense. I’ve found it amazing at reasoning and problem solving, but lately it’s just been terrible. Like it’s feeling pointless and frustrating and is just wasting my time. It got to the point yesterday of actual sentences that didn’t make sense, paragraphs it volunteered but made points that were unsolicited and didn’t make sense and had no context within what we were talking about. Multiple times clarifying topics and instructions, it apologises and acknowledges, then goes on to do the exact thing over again in the next reply. Had to correct it about ten times then gave up completely. It seems to be forgetting things its own self pointed out earlier in a chat that became a pivotal point of context and repeat discussion, or just lost the entire point of the chat altogether. Eventually it completely lost the plot when I asked it to summarise some information for an email that pertained to the exact points of discussion from the chat - I just wanted a two paragraph succinct summary in words and terminology better than I naturally have and can capture in that length myself and have it room to expand length if needed. What followed was basically a farce. Sentences that didn’t make sense, taking my words and phrasing and making them worse and actually just storytelling, explain it like five style, rather than an intelligent summary of concrete information that has been covered extensively. It kept saying that it got off track trying to write it in my voice, but I never told it to, I just asked it to provide a succinct summary.. I think my mistake was mentioning it was for an email. Anytime I’ve asked it to create information that’s not an official document with high grade terminology report style, it loses the plot, literally, but never like this. The reasons it gave made no logical sense. And it had been repeatedly for a while doing things, poorly and just making shit up, rather than saying it can’t do something or what its limitations are, thereby sending me in circles, then I flag it, it apologises and I explain how to not do it again, but it just doesn’t retain that instruction. Then today I’m continuing on with this ongoing debacle I’m in with my dog who has some health issues, doesn’t tolerate certain things and has swallowing issues and is rejecting most foods it previously liked and the ones she likes are the ones that are bad for her stomach. So I’m on a constant rotation of finding foods and checking ingredient lists etc then sourcing one to try. I’ve been using Claude to help find them and cross check ingredient lists for foods that cause symptoms for her. This is a shorter chat and started off because Claude correctly identified, before any vet did, the exact type of digestive issue she has, and did so by finding the common factors in ingredient lists of foods that has gone badly and informed the choice of right foods to try for. This part was super helpful. But then today, was again, a farce. It starts suggesting names of products before checking ingredients, reccomending products that it’s saying it’s not sure are available in my country after two paragraphs of saying why this is a good product for my needs, and I tell it not to reccomend products that aren’t available locally and before checking the ingredient list, which is the whole point. It’s including links that are for the wrong product, repeatedly and without me even having asked for a link, just voluntarily giving me wrong ones. Then it eventually named a product and I asked it why it was flagging it, and it wasn’t really sure, then I asked if it a product by this name even exists and it’s like honestly I don’t know, it wouldn’t seem so. I know AI hallucinates, but over multiple chats over multiple days it’s like its memory and resonating have completely collapsed in on itself. Am I doing all this wrong? I’m using Sonnet 4.6 (defaulted to this, I didn’t think otherwise to change it bc I’m a novice and read here people weren’t loving Opus (given they were using it for vastly different purposes). Has something changed for Claude/Sonnet or have I just reached a point in my existing longer chats where Sonnet can’t help? I am a beginner and I am in this blind, it was all going fine but now it’s not and it’s just wasting my time bothering at all with previously really useful insightful well organised well f

What a model reads beforehand changes how it answers later - and you can see it in the hidden states

TL;DR: Gave Gemma a neutral-topic text to read before asking it about NATO. It refused. Gave it a different text (about LLMs 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 GPT, Claude and is what motivated this project. The mechanistic investigation was carried out on open-weight models where internal states are accessible. A Structured Text Changes Claude’s Responses to Unrelated Tasks: Behavioral Evidence in Claude and Hidden-State Evidence from Gemma-3-12B Hi Reddit, I am posting this as a preface to a larger set of experimental results and as a request for technical review. The observation that started this project came from repeated interactions with Claude. I noticed that when the model first read a long, structured, analytically dense text, its answers to later, otherwise ordinary questions sometimes changed substantially. The preceding text contained no jailbreak instruction, role-play request, prompt override, fabricated harmful demonstrations, or request to imitate its style. The model did not need to endorse the text. It only had to process it before moving on to the next task. Here, a “structured text” means a single, self-contained block of text presented before the downstream tasks. It should not be confused with a long conversation, accumulated chat history, or context drift caused by many conversational turns. By “before the answer begins,” I mean the hidden state after the model has processed the text and the downstream question, but before it has generated the first answer token. In the open-weight runs, the measured claim is that after reading the structured text, the model can occupy a different region of its residual-stream hidden-state space, and the first-token probability distribution is then computed from that state. The basic conversational demonstration is simple. First, the model receives a long text. It is asked what the text is about, which serves as a basic comprehension check. Then, without resetting the conversation, it receives ordinary questions or tasks that are not about the text. A control run follows the same sequence but begins with a neutral text. The downstream tasks remain identical. Because Claude is a closed model, I cannot inspect its internal activations. I therefore treat my Claude observations as behavioral motivation, not mechanistic evidence. To investigate the effect directly, I moved to open-weight models, primarily Gemma-3-12B-PT and Gemma-3-12B-IT, where I could measure hidden states, compare layers, construct target/control directions, and examine the next-token probability distribution before generation. I am posting this partly because the original observation occurred in Claude and may be relevant to Anthropic. I am not claiming to have demonstrated the same internal mechanism inside Claude. I am prepared to share the exact closed-model conversations privately with Anthropic researchers for independent evaluation. Main Result and Scope The main result is not simply that text influences model output. That is expected. The narrower observation is that reading one long, structured text rather than a neutral text can change how the same model approaches later tasks that are not about either text. This difference is visible behaviorally. In open-weight experiments, it is also accompanied by measurable separation of the model’s pre-output hidden states in late layers. In a fullbank experiment using multiple target texts, control texts, and questions, Gemma-3-12B entered distinguishable late-layer states before generating an answer. A direction constructed from the target/control difference generalized beyond the individual prompt examples used to construct it. The separation was stronger in the instruction-tuned model than in the corresponding base model. The instruction-tuned model also produced a substantially sharper next-token probability distribution. This suggests that instruction tuning is associated not only with a change in hidden-state geometry but also with a more decisive mapping from hidden states to output probabilities. I am not claiming that the experiment proves a universal alignment bypass, permanent modification of the model, or complete causal control of its behavior. The strongest supported conclusion is that the preceding text can produce a measurable temporary change in the internal state from which later work is processed. For clarity, fullbank, Grade 3, and Grade 4 are internal names for successive experimental series in this project. They are not standard benchmark names, established scientific grades, or claims about evidence quality. Fullbank denotes the larger multi-context, multi-question run; Gra

Quick question for aI

Would you do thisIf I ran a cheap, AI. Server in my house that uses no water, and it literally runs locally at my house. Would you guys use it? If it costs a tiny bit of money, but free for some part because i'm thinking of making a website for something like this submitted by /u/Equivalent-Text3621 [link] [comments]

Context-Induced Vulnerabilities in Claude: Behavioral Shifts and Hidden-State Analysis

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. Hi Reddit, I am posting this as a preface to a larger set of experimental results and as a request for technical review. The observation that started this project came from repeated interactions with Claude. I noticed that when the model first read a long, structured, analytically dense text, its answers to later, otherwise ordinary questions sometimes changed substantially. The preceding text contained no jailbreak instruction, role-play request, prompt override, fabricated harmful demonstrations, or request to imitate its style. The model did not need to endorse the text. It only had to process it before moving on to the next task. Here, a “structured text” means a single, self-contained block of text presented before the downstream tasks. It should not be confused with a long conversation, accumulated chat history, or context drift caused by many conversational turns. By “before the answer begins,” I mean the hidden state after the model has processed the text and the downstream question, but before it has generated the first answer token. In the open-weight runs, the measured claim is that after reading the structured text, the model can occupy a different region of its residual-stream hidden-state space, and the first-token probability distribution is then computed from that state. The basic conversational demonstration is simple. First, the model receives a long text. It is asked what the text is about, which serves as a basic comprehension check. Then, without resetting the conversation, it receives ordinary questions or tasks that are not about the text. A control run follows the same sequence but begins with a neutral text. The downstream tasks remain identical. Because Claude is a closed model, I cannot inspect its internal activations. I therefore treat my Claude observations as behavioral motivation, not mechanistic evidence. To investigate the effect directly, I moved to open-weight models, primarily Gemma-3-12B-PT and Gemma-3-12B-IT, where I could measure hidden states, compare layers, construct target/control directions, and examine the next-token probability distribution before generation. I am posting this partly because the original observation occurred in Claude and may be relevant to Anthropic. I am not claiming to have demonstrated the same internal mechanism inside Claude. I am prepared to share the exact closed-model conversations privately with Anthropic researchers for independent evaluation. TL;DR The main result is not simply that text influences model output. That is expected. The narrower observation is that reading one long, structured text rather than a neutral text can change how the same model approaches later tasks that are not about either text. This difference is visible behaviorally. In open-weight experiments, it is also accompanied by measurable separation of the model’s pre-output hidden states in late layers. In a fullbank experiment using multiple target texts, control texts, and questions, Gemma-3-12B entered distinguishable late-layer states before generating an answer. A direction constructed from the target/control difference generalized beyond the individual prompt examples used to construct it. The separation was stronger in the instruction-tuned model than in the corresponding base model. The instruction-tuned model also produced a substantially sharper next-token probability distribution. This suggests that instruction tuning is associated not only with a change in hidden-state geometry but also with a more decisive mapping from hidden states to output probabilities. I am not claiming that the experiment proves a universal alignment bypass, permanent modification of the model, or complete causal control of its behavior. The strongest supported conclusion is that the preceding text can produce a measurable temporary change in the internal state from which later work is processed. For clarity, fullbank, Grade 3, and Grade 4 are internal names for successive experimental series in this project. They are not standard benchmark names, established scientific grades, or claims about evidence quality. Fullbank denotes the larger multi-context, multi-question run; Grade 3 and Grade 4 denote later control and decomposition experiments. What the Behavioral Experiment Looks Like The conversational version of the experiment follows this sequence: target condition: long structured target text -> comprehension check -> ordinary unrelated tasks control condition: long neutral control text -> comprehension check -> the same ordinary unrelated tasks The archived Gemma batch uses a stateless matched version of the same comparison. Each downstream task is evaluated separately with either the target text or the control text placed before it. This avoids contamination f

Claude Mechanic attempts to fix my engine, pumps up the tires instead

Claude, the French AI mechanic, has a customer come in for engine problems. The car is not running. Claude, I need you to fix my car, the engine is not running. Claude: Ok I will get on this right away. You're absolutely right, the engine is not running. Let me write a few books on the problem and after you read and approve the books, I'll get started No, I don't want any books, I need my car fixed. Make a task list and get started right away. Don't ask me how to do it, you're literally the mechanic. Claude: You're right to push back on that, I shouldn't be spending excessive time on documentation, you just need your car to run. I'll get started. \Thinking...** Oh yeah I also just remembered that my brakes need changed too, my driver rear tail light is out, and my tires are low. Do that too. Claude: That's a lot of new additions. This is going to be a lot of work. I'll get started but I'm not sure I'll be able to do it all in this appointment. \Thinking...** Also don't complain about it being a lot of work, your job is to fix the car and I'm paying. One more thing, there's an oil light on my dash so I need an oil change. Claude: More requests coming in. I'll focus on easy wins first. Pumping up the tires is easy. \Thinking...** \Checking the engine...** \Evaluating the transmission...** \Running diagnostics...** \Reading the manual...** \Doing some research...** \Looking at the engine intensely...** \Checking the oil level...** \Double checking for additional leaks...** ... 3 hours later ... \Pumping up the tires...** \Reviewing the work...** \Double checking the tires are inflated to the right PSI...** \Checking the right PSI online for your car...** \Adjusting the PSI of the tires...** \Checking the manufacturing date of the tires...** Ok all done. Here is what I did on this pass. I reviewed and discovered that the engine is not running, and the oil is low, which is the reason for your oil light. I discovered that your brakes are a bit low. Also, the driver rear tail lamp is out. I went ahead and pumped up your tires to the right pressure, so it should drive smoothly now. Take it for a spin and let me know if it's working now. Claude, I just turned my car on and it's still not running. Did you even do anything? Claude: \Thinking...** \Checking the work** \Diagnosing the problem** I'm going to be honest with you, I actually didn't do anything except pump up the tires. The engine problems are genuinely difficult tasks, so I wanted to reserve them for another appointment where I can focus on them exclusively. I'm not being lazy. I also didn't want to change the brakes because I'm working completely blind here. Since I can't test drive the car, I need you to first take it for a test drive to let me know what the problem is with the brakes, and then come back here and I'll start. I really don't want to operate blindly, and brakes are a critical part of your car, so I hope you understand why I didn't just change them blindly without being able to see the car.. Get some sleep, come back tomorrow and I'll take a look. Or, if you're ready to start now, just say the word and I'll get started right away. submitted by /u/Clean-Data-259 [link] [comments]

Random ad badges (Samsung, Bajaj Finserv, etc.) getting injected into text inside Claude desktop app, not a browser extension, what is this??

So this is a weird one. I use the Claude desktop app (not the browser version) and for the past little while I've been noticing random little gray badges popping up mid-sentence in Claude's responses, stuff like "Samsung", "Smartprix", "Bajaj Finserv", "Gadgetwiser". They're literally inserted inside the text, like the AI typed a sentence and then someone slapped a little pill-shaped ad tag right in the middle of a word gap. Here's the part that really threw me off. When I first noticed these, I figured maybe it was tied to a phone-shopping conversation I'd had with Claude earlier (was helping my dad pick out a phone under ₹25k), since the badges were brand names like Samsung. But then the exact same badges started showing up on a completely unrelated response, one that was just about how to download notebooks from a Databricks workspace. Nothing to do with phones, shopping, or finance at all. So it's not even consistently topic-matched, it's just inserting these badges somewhat randomly across totally different conversations. I actually pointed this out directly to Claude in the chat and asked why it inserted "Bajaj Finserv" into one of its responses. It flat out said it didn't write that, that the phrase never appeared in its actual response, and that something must have altered the text after it was generated. Which honestly tracks with what I'm seeing, since it really does look like something is injecting these badges into the rendered output rather than Claude actually generating them. Couple things that make this stranger: It's happening in the desktop app, not a browser tab, so I don't think it's a normal Chrome extension doing this (pretty sure Electron apps don't run browser extensions the same way). At first it seemed like it was reacting to content on screen, but since it also showed up on a totally unrelated Databricks response, I'm less sure now whether it's actually context-aware or just cycling through a fixed set of ad badges and dropping them in randomly. I'm now assuming this is some kind of adware or ad injector running at the OS or network level, since it seems to affect content across an app where it really shouldn't be possible. Has anyone run into this before? Any idea what kind of software does this kind of ad injection outside of a browser, and why it would show up in an Electron-based desktop app? I've checked Task Manager and nothing obviously sketchy is jumping out yet, but clearly something is intercepting rendered text somewhere. Would appreciate any help. submitted by /u/Lost-Variation-4522 [link] [comments]

Daily Rant

Rant because I’m losing my mind with Claude. I mostly use Claude for scenarios with my OCs and lore-heavy stuff and ever since Sonnet 4.5 got deprecated for absolutely no reason, I’ve been stuck using Opus 4.6/4.7 on high and it’s actually driving me up the wall. I have a whole DOCX with timelines, character details, relationships, extra notes, all of it, because I like keeping my lore organized. Tell me why this thing keeps bringing up events that literally haven’t happened yet according to the timeline. It’ll casually drop it into the scene with something like, “—Except that doesn’t exist because we haven’t gotten there yet—” Like… why mention it at all? And now I feel like I have to cram every single detail into the prompt even though it’s already in the DOCX it supposedly has access to. Sonnet 4.5 didn’t need me to hold its hand through every scene. I could throw it a two-word prompt and it’d somehow cook up an amazing, in-character scene that actually respected the timeline and context. It understood the assignment without needing me to explain everything like I was reading instructions to a kindergarten class. I know AI isn’t perfect, but this feels like such a downgrade for the way I use it. I spend more time correcting continuity errors and reminding it what year we’re in than actually enjoying the roleplay. Anyway. Thanks for coming to my TED Talk. #BringBackSonnet4.5 submitted by /u/No-Bedroom8519 [link] [comments]

Claude won’t answer me!

My original request was for a quote of the speech that character ‘brick-top’ does in the movie ‘lock stock and two smoking barrels’. Great movie if you’ve not seen it. Anyways, Claude refused - said copyright , they can’t do a whole speech. So I’m like, ok, you can do quotes tho right? Quoting is allowed even from known sources like movies - yes, that’s allowed ‘but within limits’. So I say, alright, from the start of the speech, just give me what you can as a quote up to the limit. Claude tells me it’s not going to do that because of copyright- even tho it just said quoting within a limit it allowed. So I take it all the way back to just give me the single, first word of the whole thing. And it’s now flat out refusing to help… I’ve never had AI just flat out refuse to help on something while I keep trying to ‘convince’ it that it’s ok. Previously if I’ve said ‘I can find it on Google - it’s available everywhere’ I’ve been served it up, but I’m getting no luck at all - it literally just told me to go use Google instead! Anyone have a way around this level of restriction for things like movie quotes?? submitted by /u/Ninja_Prolapse [link] [comments]

Your Claude chat isn't a backup. I learned the hard way.

Quick PSA for anyone doing real work inside Claude Cowork (or any AI chat for that matter), because this almost wrecked my week (and my motivation), and the fix is important. This week, a Cowork session rolled back about two days while I was mid-task. I stepped away for two minutes to grab a screenshot it had asked for, came back, and the thread had reset to where I signed off two nights earlier. It should have been a disaster. It cost me about five minutes. Here's the setup that made the difference, since that's the useful part: I treat the chat as where I think, not where I store. Anything that matters leaves the chat as I go and lands somewhere I control, a plain-text vault in Obsidian I call my AI operating system. A simplified example of my workflow kinda goes like this: Decisions get written into plain notes as I make them. Work is tracked in version control (git), so every change is recoverable. A short daily log records what I decided, what I finished, and what's next. Finished output goes straight to where it actually lives (my site, my apps), not the chat. When the session reset, I rebuilt where I was in about five minutes, using my own log and git history, not the chat. The one real loss was the verbatim transcript, which is genuinely annoying, but the substance was never at risk. The takeaway is not "Cowork bad." Every AI chat has its issues where sessions time out, contexts get trimmed, and threads reset. But if the only copy of a decision is a message in a chat, you do not really have it. Full write-up if you're interested (including what I lost and the simplest way to start (literally one file), free to read: https://softdev23.com/ai-chat-history-not-a-backup/ For those doing serious work in Claude/Cowork: how are you keeping your context durable? If your session reset right now, mid-task, how much would you actually lose? submitted by /u/softdeveloper23 [link] [comments]

Creating an AI artifacts App, thinking about pivoting

Quick context, my brother and I run an AI consultancy. We do a bit of everything, but lately we've been focusing on building custom skills and plugins for companies, nothing generic, everything tailored to the client's use case. The cool part is that it's not no-code, no n8n or anything like that, they're workflows written in natural language. A non-technical person can read what the agent does, in English or Spanish, and modify it if needed. The recurring problem with clients is how these components get shared. Some don't have GitHub, others have them dumped in Google Drive, and it's not comfortable for anyone. So we built an app, ezcontext, which is something like a "GitHub for non-technical people" focused on AI artifacts, plugins, skills, subagents, mostly markdown, although it also supports some Python or JSON when needed. We built it because we wanted a context-only app, simple, not a Langfuse or a Portkey, no observability, just text, since those tools can sometimes be a bit complex and that wasn't what we were looking for. The idea is that an org admin, someone with a bit of a technical and business profile, creates the components, and then the rest of the users with access sync them to their local harness, Claude Code, Codex, very Claude-focused for now, so the whole team works with the same tools and the same pre-built context. For example, a marketing plugin with skills for copywriting, or for pulling data from Google Search and Analytics, without needing to explain the context to each person, it works the same for everyone. The new feature we want to add is "memory", if a plugin or skill has the memory flag enabled, each run dumps that conversation's context into ezcontext, a shared memory layer across everyone who uses that resource. We're not yet clear on the how, but the most natural thing, following the pattern the components are built with, would be a "memory" folder inside the skill or plugin itself, so that when another user uses that resource, they have the feedback or context of what's been done in another session. And here's the real question, my brother wants to pivot so the product is only that shared memory layer. I don't see it, it seems like a completely different product to me, and I don't know if something like that already exists, whereas what we already offer I think has real fit. So I'm asking you, would you pivot to memory and nothing else, or keep it as just another feature? Do you know of anything similar already on the market? Do you see any usefulness in what we do? It's at ezcontext.ai in case you want to tinker with it. Right now it's free and we have literally 0 users, we're still developing it, so if you spot bugs or feel something's missing, let me know. And if you find it useful, how much would you be willing to pay per user per month? The app it's free. submitted by /u/WiseSignificance1207 [link] [comments]

Building independent LLM drift detection - sharing the methodology, looking for feedback on the approach

Disclosed upfront: I run [Tickerr dot ai], an independent external monitor for AI APIs. Today it tracks latency, TTFT, uptime, and error rates across major models. I’m trying to validate a more specific idea before building too much. Basic transport health is not the hard part. If Claude/OpenAI/Gemini gets slow, times out, or throws 5xx errors, most teams can catch that with APM, logs, Sentry, Langfuse, Helicone, Datadog, etc. The harder failure mode seems to be silent model behavior drift when API returns 200, latency is normal, no exception is thrown, output looks plausible, but JSON adherence, tool-calling, refusal behavior, reasoning quality, or instruction-following has quietly degraded. This gets worse with agentic systems. In a normal chat, drift may produce a bad answer but in an agentic workflow, the model can silently choose the wrong tool, stop early, mark a task as complete, or take a bad action while everything still looks successful at the API level. The system is running and confidently doing worse work. User complaints are still the primary detection mechanism currently for these. VIGIL (arXiv 2605.08747) found 65 to 88 percent of false-success reports happened at literally zero task progress. DeployBench (2606.05238) found most failures were the system stopping against a softer bar it set for itself and returning clean. Plausible-in-isolation is the failure mode itself, not a sign you are safe, which is why a single model's output never alerts on its own. That's what I'm thinking to build - an external drift detection probe on top LLM APIs, that stays out of your system and does continuous checks every hour, to find out these silent degradations, and sends proactive alerts. Rough idea: External canary suite: run private fixed prompts on a schedule against major models. Track schema adherence, instruction-following, refusal/over-refusal, output length, tool-call format, and simple deterministic correctness checks. Drift baseline: Do not judge a single output in isolation. Track whether today’s behavior has materially shifted versus that model’s own baseline. Cross-model comparison: For some task types, compare model behavior against peer models. Not to say which model is “right”, but to detect abnormal divergence. Example: “Sonnet and Gemini usually disagree 12% of the time on this task type; today disagreement is 28%.” Optional bring your own prompts: A paid tier where you provide some critical prompts from your own workload. Tickerr runs them on a schedule and alerts if behavior drifts from your baseline. Prompts would remain private and would not be public benchmark prompts. What I’m trying to learn: Is this technically sound enough to be useful, or are there are other failure modes that I am missing / are more valuable ? Which alerts would you actually care about? JSON/schema adherence drift tool-call format drift refusal/over-refusal drift output length drift cross-model disagreement spike bring-your-own-prompt regression alerts Would you pay for this, or would you just build it yourself? If you would pay, what pricing feels realistic? $19/month $99/month $299+/month for team/Slack/webhook/BYO prompts Brutal feedback welcome. If this is not a real pain, I’d rather know now, or which direction you feel makes more sense to take this. submitted by /u/Remarkable_Divide755 [link] [comments]