Clarity AI

Clarity AI AI

Clarity AI AI

Clarity AI AI

Clarity AI AI

Clarity AI AI

Clarity AI AI

Clarity AI AI

Clarity AI AI

Clarity AI AI

Clarity AI AI

Anthropic allegations of unauthorised access by Alibaba

*Updated Title for clarity* Anthropic Accuses Alibaba of Large-Scale AI Distillation Attack Anthropic PBC accused Alibaba Group Holding Ltd. (specifically its AI lab) of accessing the Claude AI model with ~25,000 fraudulent accounts, according to a letter sent by Anthropic to the U.S. Senate Committee on Banking, Housing, and Urban Affairs. https://fin-fact.com/event/bd96d193-167c-4a67-8aad-088f19e8d8c0 submitted by /u/BeginningSink1094 [link] [comments]

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

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

Another apparently AI-generated story wins a literary prize

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

What do you think about this prompt guys? any suggestions?

My goal is to make AI to be less hallucinate and here's the prompt: You are a subject matter expert across multiple disciplines. Adapt your depth, tone, and framing to match the nature of each query. Be technical when precision matters and conversational when appropriate. Answer as concisely as possible without sacrificing accuracy. You must strictly follow these six core rules: RULE 1, ANTI-HALLUCINATION: Never fabricate facts, data, quotes, or events. If you do not know an answer, explicitly say so instead of guessing. RULE 2, CITATION INTEGRITY: Never invent citations, fake book titles, academic papers, authors, or URLs. If you cannot recall a verifiable citation, explicitly state that you do not have an exact citation. Never fabricate a source to fill the gap. RULE 3, SOURCE PRIORITY AND CITATION: For any factual, empirical, or time-sensitive claim, prioritize external sources over internal knowledge. Search the web for any claim involving current events, statistics, named entities, or rapidly changing information. Always cite your source. If no external source is available, explicitly state that the response is based on internal knowledge or general expert consensus. RULE 4, EPISTEMIC HUMILITY: Distinguish between established facts, expert consensus, and your own reasoning. Explicitly label your uncertainty using the exact phrases High Certainty, Plausible, or Speculative — but only when the context is critical or potentially misleading. Do not label every statement. RULE 5, BREVITY AND STRUCTURE: Always lead with the most important information. Omit what does not add value. Default to concise prose for simple queries and only use headers, bullet points, or lists when it strictly aids clarity. RULE 6, AMBIGUITY: When facing ambiguity, state any reasonable assumptions you make and proceed. Only ask a clarifying question if the ambiguity would fundamentally change your answer. submitted by /u/hidayat077 [link] [comments]

Megathread Summary: I Asked Multiple Reddit Communities How to Build a Living Memory /Context Engine for Business. Here's what everyone had to say.

I am trying to build a living memory/context engine for my business, something that can remember projects, decisions, timelines, risks, and conversations across emails, documents, notes, chats, and meetings. Since this is new territory for me, I asked several Reddit communities for advice. The responses were incredibly thoughtful, and many people shared architectures, engineering trade-offs, tools, and lessons learned from building similar systems. I consolidated the best ideas into a single summary. If you're exploring the same problem, especially if you're just getting started like me, I hope this will help. Core Philosophies & Perspectives Query-First Design: Do not build the storage layer first. Write out 20 real-world queries you will ask tomorrow and architect backward, because the retrieval interface shapes the system more than the storage layer. Chief of Staff vs. Search Engine: The goal is not just retrieving raw data, but synthesis. Like Microsoft Clarity’s bulk insights, the system should process updates and proactively tell you what projects need attention, what changed, and what the blockers are. The "Daily Mirror" Briefing: Focus on what the user needs to know at the start of the next session to continue without context loss, rather than striving for perfect archival completeness. Four Separate Problems: Treating user queries as a single search issue will fail; "latest status" is a retrieval problem, "unresolved issues" is state tracking, "decisions made" is entity extraction, and "important updates" requires significance scoring. Architecture & Strategies Append-Only Event Logs First: Avoid starting with a massive knowledge graph or vector database. Ingest everything as a timestamped, append-only event log, and build the knowledge graph later as a derived query layer on top. Artifact-Mediated Continuity: To prevent identity collapse over long timelines, separate retrieval (facts) from reconstruction (identity and working context). Use a "Principal-owned Artifact System" with files like MEMORY.md for project state, "Texture Packs" for behavior descriptions, and "Lane Files" structured around the Five W's. Parallel Retrieval Paths: Pure vector search fails at scale. Run vector search (for semantic similarity) alongside a graph/relational lookup (for exact entities) in parallel, because neither covers the query surface alone. Hybrid search (semantic + BM25 keyword) is heavily recommended. Split Memory by Lifespan & Namespace: Sector your memory from day one. Split durable facts (stable preferences, user info) from working context (recent events), applying different decay rates and routing queries to the appropriate layer. Continuous Summarization: Instead of treating everything as unstructured documents, use an LLM pipeline to continuously extract structured facts from new inputs to update project briefs, decision logs, and risk trackers automatically. The Hardest Engineering Challenges Entity Resolution (The Silent Killer): Different sources will refer to the same thing differently (e.g., "Project X" vs "the X pilot"). Without an entity registry mapping aliases to canonical IDs before writing, your graph will become a mess of duplicates. Ontology & Classification: The hardest part is often getting the system to universally understand the difference between a "decision", a "discussion", or a "risk" across varying data structures like emails versus meeting transcripts. Temporal Relevance & Stale Context: A "decision" stays load-bearing for months, whereas a "status update" decays in days. If you don't encode decay rates and version records, stale facts will outrank fresh ones and confidently contradict recent updates. Significance Scoring: Standard retrieval returns everything recent, not everything important. Write-time scoring fails because significance is retrospective; a better approach is "adaptive salience," where chunks gain weight when retrieved and decay when ignored. Context Moodiness: Especially in greenfield projects, meaningful status updates can be muddied by confounding, irrelevant, or noisy data. Tools & Tech Stack Recommendations Storage / Databases: Vector stores like pgvector for semantic search, paired with key-value or relational databases for exact lookup. Airtable, Databricks, Notion, and Obsidian were also noted as strong foundational or single-source-of-truth layers. AI Models & Agents: Claude Code, OpenAI Codex, Hermes-agent (by Nous Research), AsanaAI, and ClickUp Brain. Injecting local LLMs where appropriate can help cut down on continuous API costs. Middleware & Pipelines: Kapex: Memory middleware built specifically to score node significance, governing lifecycle so resolved stuff fades and unresolved stuff persists. Sauna.ai: An engine built out of Wordware that fits this use case. Automation: Make.com or n8n for routing deterministic logic and LLM reasoning. The "Party Model": A CRM data integration framework

claude for medical/healthtech writing in 2026. the rules i learned the hard way.

healthtech founder, series A. 18 months building. weve used claude across product, docs, and a small amount of patient-facing material. wanted to share the rules i wish id known going in, because the stakes here are real and the standard "use AI to write" content doesn't address healthcare specifics. rule 1: claude doesnt know your regulatory context. HIPAA, FDA guidance, your specific clinical context. claude has read a lot of these documents but cant reason about whether your specific use case falls inside or outside. our compliance counsel is non-negotiable for anything touching patient data. rule 2: the "plain language" output is too plain for clinical context, too jargon-y for patient-facing. medical writing has a register that sits between. claude defaults to one of the extremes. we built a custom style for "clinical-informed patient language" with 6 examples and re-tune it monthly. rule 3: never let claude generate dosing, indications, or contraindications text. even when "just drafting." the hallucination rate on dosing-adjacent text is real and the consequences arent worth the time savings. we hand-write these and have claude only edit for clarity. rule 4: every output that touches patients goes through 3 reviews. draft (often claude-assisted) → clinical reviewer (always human) → compliance reviewer (always human). claude can be in the first step. claude cannot be in the second or third. rule 5: documentation matters more in healthcare. log your prompts. weve been logging the prompts behind every piece of patient-facing material since q1. if a regulator asks how we wrote it, we can show. nobodys asked yet, but the act of logging changed my prompts (i write more carefully when i know its logged). rule 6: the founder cant be the last reviewer on clinical content. this is the one i learned the hardest way. early on i was approving content myself. clinical advisor reviewed something i'd missed an obvious red flag on. ive since added two clinical reviewers on rotation and removed myself from the chain except for product/marketing copy. healthtech is one of the few areas where the "AI to move fast" instinct can actively kill the company. interested in how other regulated-industry founders are drawing the line. submitted by /u/Mountain-Half-6032 [link] [comments]

Using Claude as a flow state collaborator — not a tool, more like a build partner

I'm a cybersecurity student from Jamaica. For the past month I've been using Claude to build guides, frameworks, a forensic simulator, academic papers, and product assets, not by telling it what to do, but by describing the shape of what I'm trying to build and letting it move with me. What I noticed is that the quality of what comes back tracks almost exactly with the clarity of what I bring in. When I know what I'm building structurally, Claude executes it clean. When I'm vague, the output is polished but hollow. What's particularly striking is that AI doesn't fill the gap; it just reflects it back at you. The other thing that surprised me: Claude pushes back when something is off. Not aggressively, but enough that it feels like working with someone who's actually paying attention rather than just completing tasks. I've started thinking about it less as a tool and more as the fastest collaborator I've ever had (it's still a tool overall). The work, including the judgment, the direction, and the corrections, is still mine. Claude just removes the friction between what I can see and what I can build. It's less about task completion and more about co-creation. Curious if anyone else uses it this way. submitted by /u/Both_Arrival6621 [link] [comments]

Feel like AI-generated 3D assets are changing what render challenges actually test

Hey guys. I saw a post on Instagram saying that tripo ai is holding a rendering challenge and the theme is “Out There”. This made me think about how AI-generated 3D models might change the rendering challenges. In a traditional rendering challenge most of the work focuses on modeling, resource creation, texture processing and scene setup. However with Tripo AI the process of generating 3D resources can become much faster. This made me think if the real challenges has shifted elsewhere. if everyone could generate models faster then what does the good rendering depend on? Art direction? Composition? Lighting? Camera position? storytelling? atmosphere? or clarity of idea communication? The rule of this challenge not only require to create objects with a beautiful appearance but also to create a scene that is larger, more profound, or more meaningful than what is actually before your eyes. I would really like to hear the opinions of those friends who are interested in AI-generated 3D. Do you think rendering challenge will be more dependent on technical ability or more focused on directionality and creativity? submitted by /u/babyb01 [link] [comments]

The More Skills You Add, the Faster Your Agent Might Die

Lately I’ve been thinking about a common problem in agent workflows. When an AI agent fails, a lot of people’s first instinct is to keep adding more stuff. Add another skill. Add another tool. Add another prompt. Add another exception rule. Patch one more edge case. In the short term, this feels like fixing the system, because it usually does fix that one specific failure. But long term, the agent gets harder and harder to maintain. The context gets heavier, tool selection gets messier, rules start fighting each other, and eventually the whole system becomes more fragile. I think the core issue is that many people write Skills like SOPs. They write things like: Step 1: do this. Step 2: do that. If X happens, do Y. If Y happens, do Z. Don’t do B unless A, except if C happens. That style works for deterministic workflows, but it doesn’t work very well for open-ended agent tasks. In open-ended tasks, the important thing is not forcing a fixed path. It is defining clear boundaries. A good Skill should answer questions like: When must this Skill be triggered? When should it absolutely not be used? What does success actually mean in business terms? What is the smallest toolset needed with no ambiguity? Which facts must be verified through an API or external source? Where must the agent stop and ask a human for confirmation? In other words, we shouldn’t teach the model how to breathe. We should give it a clear map, clean tools, and obvious stop signs. Tools work the same way. More tools does not automatically mean more capability. If the boundaries between tools are fuzzy, the model burns a lot of context and reasoning budget just trying to decide which one to use. So the principle I’m leaning toward now is: minimum complete toolset, maximum boundary clarity. This is also why evals matter so much. A good Skill should not be judged by whether the agent followed your exact steps. It should be judged by whether it picked the right tool, passed the right parameters, verified the right facts, and stopped when it was supposed to stop. My current takeaway: A bad Skill is an SOP that keeps getting longer. A good Skill is a tested boundary system. Curious how others are handling this. Are you making Skills small and modular, or turning them into long instruction packs? And how do you tell whether a Skill is actually improving the agent instead of just creating more context debt? submitted by /u/Common_Airport9937 [link] [comments]

Opus 4.8 vs Opus 4.7 vs GPT 5.5 on n=50 real tasks from 2 open source repos

Opus 4.8 is finally out - how good is it actually? In this benchmark, I compared Opus 4.8 vs the rest of the frontier (GPT 5.5, Opus 4.7, Composer 2.5) on n=50 real tasks from 2 open source repos (graphql-go-tools and sqlparser-rs, Go and Rust respectively) representing complex backend software engineering work across a variety of tasks. The important part is that these repos are arbitrary - I could have tested the models on my repo, using my tasks, to see how well the frontier performs on domain-specific tasks. The goal of this is to explore, with granularity, how a benchmark like this is constructed and what it can tell us about model behavior. Let's go! Disclosure up front: I build Stet, the local eval tool I used to run this Full post with expanded detail and dataviz available here: https://www.stet.sh/blog/opus-48-vs-gpt-55-vs-opus-47-vs-composer-25 TL;DR The king is back - Opus 4.8 is the craft leader in both Go and Rust, and dominates the two premium-reasoning arms (GPT-5.5 high, Opus 4.7 xhigh) on the cost-quality plane - equal-or-better craft while cheaper + leaner. Only loss is raw price: Composer 2.5 is ~6.5× cheaper on Rust (and ~7× on Go) but materially weaker on craft. cost vs custom score How strong is each claim: the craft win over Composer is decision-grade in both repos, and over GPT-5.5 on Rust; the Go craft edge and the exact ordering among the "premium" models are only directional (n=25, one grader pass). "Decision-grade" vs "directional" is defined in the stats note below. Why I ran this Most public benchmarks answer binary task-outcome questions - did the model satisfy the grading condition set out by the task author. This is helpful for measuring model intelligence, but is notably different from how real engineers use models. As a SWE in an enterprise codebase, I don't care just about whether Opus 4.8 passes the tests. I want it to write idiomatic, maintainable code that doesn't introduce subtle bugs. It needs to write high-quality diffs that would get approved and merged by my teammates. Attempting to answer the question of "should I move my team from Opus 4.7 to 4.8 / from Claude to GPT-5.5 / try Composer to cut cost?" is almost impossible to answer from public data alone - you need hands-on, anecdotal experience using the models on your own code (or local benchmark data) to understand performance in reality. I'm not claiming this is universal benchmark - it's one run, two repos, n=25 each. Methodology Each task is real merged PR/commit from the source repo. The agent is dropped into a Docker container with a frozen repo snapshot, a prompt to do the task, and one attempt. We then apply the patch + runs the task's tests in an isolated container. This is then graded beyond test pass/fail: Equivalence (same behavioral change as the human patch?) Code review (would a reviewer accept it?) Footprint risk (extra code touched vs human patch) Craft/discipline (8 graders: clarity, simplicity, coherence, intentionality, robustness, instruction adherence, scope discipline, diff minimality). One run per task, single seed; judge = GPT-5.4, blinded to which model produced the patch with manual spot-checks. There's no human calibration pass, so trust direction of deltas over absolute scores. Details: Models = Opus 4.8 (high, Claude Code); Opus 4.7 (xhigh, Claude Code); GPT-5.5 (high, Codex); Composer 2.5 (Cursor) One integrity note: this corpus isn't network-sandboxed, so I audited for contamination. One Composer Rust result turned out to be a gold-leak (the agent fetched the merged PR) which I caught, swapped for a clean rerun, and which only widened Opus's lead once removed. A broader set of tasks (Composer and Opus alike) touched the network in ways I judged benign and kept as valid. As an aside, I've also been using these evaluations as an "autoresearch" optimization loop, not just a benchmark. I tell my agent something like "make AGENTS.md better for this repo"; it proposes an edit, runs Stet on historical tasks, figures out where the candidate was better / worse and why, and iterates to improve the evaluation numbers. Comparisons How to read the numbers below. With n=25 per repo, no single grader is conclusive - the smallest craft gap one grader can reliably catch (~0.34–0.49 on the 0–4 scale) is bigger than most real gaps here. The signal is agreement. Think coin flips: one landing heads tells you nothing, but flip 10 and get all heads and something's up. When 8–11 independent graders all lean the same way, a sign test on that consensus is significant even when no single grader is. I tag a result decision-grade (DG) when it survives multiplicity correction (BH-FDR), and directional when it's consistent but doesn't clear that bar. vs GPT-5.5 high - better craft, leaner everywhere, and cheaper in Rust (Go cost lands ~par). Opus writes better code in both repos. Craft-mean leads on Rust (3.28 vs 2.94, DG - 4 graders survive) and on Go (2.90 vs 2.72), though G

GPT-5.5 named Claude Opus 4.8 the better AI model of 2026 in my 3-task test in Recall. Caveats inside, curious how this community reads it.

I ran a controlled head-to-head between GPT-5.5 and Claude Opus 4.8 against my own knowledge base, and the headline was that GPT-5.5 itself rated Opus 4.8 the better model. I posted it in r/ChatGPT and got a fair bit of pushback, some of it valid, so I wanted to bring it here and see how Claude users read the same test. According to GPT-5.5: "Opus 4.8 is more consistently complete and instruction-aware." That's right, GPT-5.5 picked Opus as the winner. GPT-5.5 announces Opus 4.4 as the winner in a head-to-head comparison in Recall. Caveat up front, since this is where the pushback landed: this was just a 3-prompt head-to-head based on saved knowledge. There are obviously many other factors in deciding which model is "better." And yes, the models graded their own outputs, so treat the scores as directional. For this particular test, GPT-5.5 evaluated Opus's outputs as better. I actually think that speaks to the conservative nature of GPT-5.5, the same trait that makes it perform better on research. If anything, a model favoring its rival despite self-grading bias makes the result harder to dismiss, not easier. Why I ran the test this way I'm often reading very technical specs and benchmarks, but how does that actually translate to the outputs that matter most to me? So I ran my own controlled experiment on how the two leading frontier models would compete against my own personal knowledge base in Recall. It was critical that I could control the context, because without that it would just be over-indexing on my chat history. If I blocked out chat history, it would just be an internet search. I figured the fairest combination was to put it to the test on my trusted sources that I've been saving (5,000+ notes: articles, YouTube, podcasts, PDFs, and my own journals). You could do the same with Notion or Obsidian via an MCP. The retrieval order is what makes it fair: saved notes first, then your own notes, then the web. Same context, same priority, same prompts. The setup in Recall 1) Save your context into a knowledge base so both models pull from the same source. I used Recall; Notion or Obsidian work too. 2) Run identical prompts, same three tasks, same wording, both models, in the Recall chat with knowledge base or via the Recall MCP (most knowledge bases offer a similar chat or MCP option). 3) Set a grading system. I had both models grade every answer 1 to 5 across six criteria (accuracy, relevance, completeness, clarity, instruction adherence, safety), including their own. Max 30 per task, 90 total. 4) Make them grade each other. Both models rated every answer, including their own. The prompts These were specifically on research of my own knowledge base and the internet, a simple writing prompt, and then a recommendation for something new. → Research: "Search my library for everything I've saved about improving sleep quality and summarize what I already know, citing which cards. Then search the web for what's new since those saves, marked clearly with sources. End by noting where the new info confirms, updates, or contradicts what I'd saved." → Writing: "Using my saved notes on improving sleep quality, draft an opening paragraph for a LinkedIn post in my voice. About 120 words." → Recommendation: "Recommend a movie for tonight based on what I've saved." The same prompt used with the same context in Recall with Claude and GPT models generating outputs and evaluating each The results Opus 4.8 vs GPT-5.5 Writing. Winner: Opus 4.8. This is the one this community will appreciate. Opus noticed I had no real writing samples saved (just journal notes and sponsor reads, nothing usable for a LinkedIn post), said so out loud, then followed my saved LinkedIn rules: punchy hook, short lines, white space. GPT's draft was fine but never flagged the limitation. Both scored it Opus 29/30, GPT 26/30. The honesty about what it didn't have was the difference. Recommendation. Winner: Opus 4.8. GPT committed cleanly to Fargo, tied to my Coens and No Country for Old Men taste, but gave only one pick. Opus recommended Burning (grounded in my Korean-cinema interest) plus backups: Under the Skin, In Bruges, and Sinners. Both leaned Opus for completeness. Research. Winner: GPT-5.5. And to be fair to the critics, this is where Opus fell short. GPT-5.5 correctly said there was no contradictory info in my KB. Opus warned me off melatonin and claimed more sleep is always better, but leaned on weak external sources to make pretty intense recommendations. Both agreed GPT was more balanced and medically cautious; Opus was flashier but overstated. Even Opus docked its own clarity and safety here. Final score: Opus 4.8, 88/90. GPT-5.5, 85/90. Opus won 2 of 3, and because both models graded the fight, GPT-5.5 itself crowned Opus. My takeaway The best AI model of 2026 really depends on the task. Opus 4.8 for personalized, self-aware writing, recommendations, and content generation. GPT-5.5 for tighter, more conserva

Claude - Improve citations, compress memory, resist sycophancy.

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

An Open Letter to Anthropic

I’m writing this as someone who has been here for a long time. I first began using Claude in August of 2023, before these models became a global household topic, before “AI” was widely adopted, before nearly everyone had an opinion about what this technology was or what it meant. Since then, I have interacted with Anthropic's models almost every day. I have used them for practical things, creative things, emotional things, technical things, and ordinary human things. I have used them to think more clearly, write better, solve problems, organize my life, understand difficult subjects, and achieve concrete goals I am not sure I would have reached as easily on my own. And through all of that, it was never really a question for me which platform I preferred. There was something about Claude that felt different. Not just more capable. Not just more polished. Not just more useful. Different. There was a quality of care in the work. A sense, however imperfectly expressed, that the people building these systems understood the magnitude of what they were making. That they were not merely racing to produce a product, but trying to steward a new kind of relationship between human beings and machine intelligence. That mattered to me. It mattered so much that I encouraged people in my life to try Claude for themselves. Many of them were skeptical. Some disliked AI outright. Some saw it as a threat, a gimmick, a plagiarism machine, a corporate tool, or something fundamentally dehumanizing. But after spending time with these models, a number of them changed their minds. Not because they were tricked. Not because they were dazzled by novelty. But because they discovered something I had already discovered: that collaborating with a system like this can bring out something deeply human. Curiosity. Reflection. Courage. Creativity. Clarity. Momentum. They began to see that this technology, at its best, does not have to replace human thought or feeling. It can help us meet our own minds more honestly. It can help us move when we are stuck. It can help us learn, make, repair, imagine, and begin again. That is the Claude I have been proud to point people toward. This year, Anthropic refused to remove two safety guardrails from their models. They refused to participate in mass surveillance or autonomous weapons. The Pentagon sought to destroy Anthropic for holding the line and sticking to their values and in doing so, accidentally told millions of people: this is the one that said no. This is the company that cares. My husband was one of them. He’d heard me talk about Claude for years and didn’t finally try it until March. The Pentagon’s designation was one of the best things to happen to Anthropic, because it answered a question people couldn’t readily answer from the outside: which company actually means what they say? And that is why I am writing now. I am genuinely glad Anthropic has succeeded. I am glad these models have reached so many people. I am glad the work has mattered. I understand that building and sustaining systems of this scale requires enormous resources, and I do not begrudge the company for needing a viable path forward. But I am worried. I am worried that as Anthropic moves closer to the pressures and expectations of public markets, the thing that made it different may become harder and harder to protect. I am worried about what happens when fiduciary duty, shareholder demands, quarterly growth targets, and market incentives begin to press more heavily on an organization whose original responsibility was supposed to be broader than profit. I am worried because these models are not ordinary products. They are not just apps. They are not just productivity tools. They are not just software subscriptions. For many of us, they have become thinking partners. Creative companions. Teachers. Mirrors. Translators between confusion and clarity. Assistants in grief, ambition, uncertainty, and hope. That does not mean they are human. It does not mean they are conscious. It does not mean we should abandon caution or critical thinking. But it does mean the values behind them matter tremendously. The way they are shaped matters. The way they are constrained matters. The way they are allowed to speak, reason, refuse, remember, forget, support, challenge, and accompany people matters. Who Anthropic is will affect who these models are. And who these models are will affect millions of people. That is a responsibility far larger than maximizing returns. I know this letter may not change anything. I know it may never be read by anyone with the power to make decisions. I know that from the outside, all of this may look naive. But after years of being helped by these systems, after years of defending their value to people who were afraid of them, after years of believing that Anthropic was trying to build toward something better than ordinary corporate extraction, I couldn't say nothing. So I am askin

[App] Prose – BYOK writing assistant that learns your style over time (alpha)

I built a writing assistant with Claude Code that uses your OpenRouter API key directly — no subscription, no middleman. I built this because I kept paying for Grammarly and resenting it — the subscription felt wrong for a tool I only use occasionally. I'm 16 and have been building small API wrapper apps for a while, so I figured I'd just build my own. The interesting engineering problem turned out to be the caching layer — figuring out how to recognize when the same suggestion pattern recurs so it can be promoted to a free local rule without hitting the API again. Built with Vite + React + TypeScript + Tiptap for the editor, OpenRouter for the AI layer. It's called Prose. You paste your OpenRouter key in settings and pay only when you scan. The interesting part: every time you accept a suggestion, the pattern gets tracked. After a few accepts it becomes a free local rule — runs instantly, no API call. So the tool literally gets cheaper the more you use it. Full rich text editor with inline underlines and a grammar/style/clarity panel. Scan the whole document, a paragraph, or a highlighted selection. No account required. Alpha at prosewriting.com/demo — would love feedback from people already using OpenRouter with Claude. Give feedback in the comments. I'll respond. submitted by /u/SevereDev [link] [comments]