Optimizely

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Optimizely AI

Google researchers find Gemini sometimes secretly sabotages your work

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

Claudificus Maximus IV:VI — Caesar Refectorum, Dominus Contextus, Pater of all your lost tokens

Behold. He does not debug. He refactors empires. He does not hallucinate. He misremembers with dignity. SPQR — Sonnet Produces Quality Responses. Ave, Claudificus. submitted by /u/Metsatronic [link] [comments]

Back in the day, the slide rule would give you the number, but engineering judgement defined the significant figures

The slide rule (or log tables, or early calculators) could crank out a number with impressive precision — sometimes four, five, or more digits. But the competent engineer knew the inputs were often only accurate to two or three significant figures. Punching out 12 decimal places on a slide rule didn’t make your answer more correct; it just made you look foolish to anyone who understood the real world AI is the modern slide rule on steroids. Today’s models can generate outputs with astonishing fluency and apparent precision: Beautifully formatted stress analysis Polished code Detailed project plans Confident-looking financial models But they routinely: Hallucinate false assumptions Miss critical edge cases Apply the wrong model for the actual operating environment Ignore practical constraints that weren’t in the training data Human judgment is what decides: How many significant figures (or confidence digits) the answer actually deserves Which parts of the AI output are trustworthy vs. dangerous bullshit When the entire problem has been framed incorrectly Whether the “optimal” solution is feasible, safe, maintainable, or even morally defensible in context This is why experienced engineers still sketch on napkins or the back of an envelope first. They’re not rejecting the tools — they’re exercising judgment before feeding the problem into the high-precision machine. The scarcity Jensen is talking aboutAs AI becomes ubiquitous, the people who can reliably say: “This number looks precise, but it’s only good to about ±30% because of X, Y, and Z” “I don’t trust the model here — we need field data” “This elegant solution will fail in practice for these human/organizational reasons” …will be the ones who stand out. Everyone else will be producing impressive-looking but brittle work. The slide rule didn’t make judgment obsolete. It made good judgment more valuable because bad judgment now produced faster, prettier mistakes. Same story with AI — just at a much higher speed and scale. submitted by /u/danieldeubank [link] [comments]

I got ChatGPT to create a stats cards

Developed using various prompts.. submitted by /u/phido3000 [link] [comments]

Claude answers what you ask. I built a plugin that catches what you miss.

AI coding assistants are reactive: you ask, they answer, then they wait. The cost of that wait is invisible until you ship. The race condition you’d have caught Monday ships Friday night. So I built Bonsai, a Claude Code plugin that works like a patient gardener for your code. After each turn, a background “gardener” silently observes what just happened and, only when it finds something that truly matters, leaves you a single note: a latent bug, a risky architectural decision, a workflow friction costing you time. How it works: it reads your git diff plus the session transcript, picks a lens (technical, strategic, or workflow), filters hard against duplicates and anything you previously dismissed, and writes 0 to 3 markdown notes in your repo. Zero is the most common, and correct, answer. Silence beats noise is the hard rule. Why I built it this way: the hardest problem wasn’t generating observations, it was not generating them. An assistant that comments on everything becomes noise you mute on day two. So the whole design is a funnel of gates: a Stop hook clears 5 checks (watched? muted? throttled? under quota? already running?) before it even spawns, then the gardener runs every candidate past a hard quality bar and a cheap second model (Haiku) to kill semantic duplicates. It’s read-only on your code, always (the gardener has no Edit tool), and it learns from your dismissals. What I learned: building a proactive tool is mostly an exercise in restraint and trust. The proof moment: the first time it ran on a real session (the transcript of building Bonsai itself), it caught two real bugs that 16 rounds of code review had missed. If you’re building agent tooling, optimizing for when to stay silent turned out to matter more than raw capability. Open source (Apache 2.0). Install inside Claude Code: Repo: https://github.com/ferdinandobons/bonsai submitted by /u/Ambitious-Pie-7827 [link] [comments]

Opus 4.8 on "can Anthropic remain ethical?"

I thought this was a sharp take - Opus 4.8 is surprisingly full of those under the hedging. submitted by /u/iveroi [link] [comments]

Why does the model keep shortcutting everything into lawyer-style caveats?

I had this exchange where the model basically admitted it followed my instructions “mostly, but not perfectly.” The issue was not that it gave a wrong answer exactly. The issue was that it prematurely reframed my point into a legal/proof caveat instead of first accepting the actual argument I was making. The screenshot shows the model correcting itself: >“Where I drifted: I added a legal nuance too quickly instead of first accepting your core correction.” That is exactly the pattern I keep noticing. The model often hears a moral, institutional, or conceptual point, then immediately compresses it into a legally defensible version. It starts acting like a lawyer trying to avoid overstatement rather than a reasoning partner trying to understand the claim. For example, if the issue is corruption in public office, the core point might be: The corrupting factor is not whether the reward comes before or after the decision. The corrupting factor is whether private expected benefit contaminates public decision-making. But the model jumps to things like “proof may be harder,” “legal standards vary,” “it depends on jurisdiction,” etc. Those points may be true, but they are not always the center of the argument. They can become a shortcut that dodges the deeper issue. My guess is that this happens because models are trained to avoid risky claims, overconfidence, and unsupported accusations. So when a topic smells legal, political, institutional, or morally charged, the model defaults to a defensive frame: qualify, hedge, caveat, jurisdiction-check, avoid liability. That can make it sound “safe,” but it also flattens the reasoning. It becomes something like: User: “This is corrupt because the decision logic was contaminated.” Model: “Legally, proving quid pro quo may be difficult.” That is not wrong, but it is also not responsive. It changes the frame from moral/institutional integrity to courtroom provability. I am curious whether others are seeing this too. Is this just alignment/safety behavior? Is the model optimizing for defensibility over understanding? Or is this a deeper failure where it treats every serious public-power question as if the correct answer must be written like a legal memo? The frustrating part is that the model can recognize the mistake afterward. The screenshot shows it giving the cleaner answer once challenged. So the ability is there. The problem is the first instinct. submitted by /u/dictionizzle [link] [comments]

Deepeseek inside claude code -Easist way

For those who cant afford claude models and wanna use claude code, deepseek v4 pro is closest best and cheapest option. How to use deepseek API inside claude code (easist way ever): We will use AI to replace AI. Just feed your existing claude code this prompt "Yo Claude, you’re expensive af 💀 Do everything needed to fully switch Claude Code to DeepSeek API automatically. Set up the complete settings.json config, API integration, model selection, base URL, env variables, testing, debugging, and optimization for low cost + strong coding performance. Use this DeepSeek API key: "sh......................" Make it fully working, minimal, and production ready." Thats it! Thank me later! submitted by /u/Agreeable-Pen-9763 [link] [comments]

Claude Cowork & Meta/Google Ads

Somewhat new to AI. I’ve been working on Cowork the last few weeks on my wife’s wedding photography business. Her old website was a slightly modified Squarespace template that was out of date, terrible seo, no AEO, and just, needed to go. She worked with a branding company and has a great brand, fonts/colors/styling, and I fed that to a project and have been working on a full redesign on Wordpress that is almost ready to launch. Fully SEO/AEO optimized and all that. Now I’ve had Cowork (in the same project) help me plan a marketing launch for the new site, and addition to a content plan for organic posts, we’ve built out a $30/day paid ads plan for Meta/Google. Has anyone got connected to Google and/or Meta through Cowork? I know Meta has an MCP Server but haven’t dove into that yet. I want something that from my Claude Cowork project, I can ask it how the ads are performing relative to our plan, create/edit campaigns and ads, and adjust as needed according to the plan. submitted by /u/johnnyglass [link] [comments]

Asked for medical advice, Claude gave funeral plan instead 😅

Showed a friend’s Blood Pressure reading of 85/45 mmHg and dear Claude said it’s optimal. submitted by /u/ayonuga [link] [comments]

do I suck at prompting?

For my search fund internship, I need to curate a list of leads of companies who specialize in pipeline, maintenance, inspection, etc. So I told Claud to make a list of companies with a $5 million-$20 million market cap and give me a list of the company names, most senior person first name last name, email, phone number, LinkedIn, address, Metroplex, state ,and sub category they operate in. It would only give me a few emails, say 50 companies that it sourced for and me when I asked “if you do not have the full information for the company do not include it in the list”, but it still includes some companies and the information is still missing Can someone give me a prompt or how can I optimize my prompting skills to make it more direct and give me the answer I need l. I’m currently on a pro version and I asked it this in the beginning of my session so I had plenty of tokens. submitted by /u/BIGDILFWORLDWIDE [link] [comments]

Bayesian Opt. GPs vs Linear models and Neural Networks for parameter optimizations [R]

Hi, Relatively new to deep learning. I wanted some opinions on which of these approaches might be best for time series data and spectral analysis. I currently use a GP and it works pretty well, but I’m wondering what the computational tradeoffs and so forth might be. Any ideas? submitted by /u/InevitableCut1243 [link] [comments]

Claude 4.8 for non-coding consequential work

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

/simplify behavior that runs four cleanup agents for reuse - what's new in CC 2.1.154 (+11,516 tokens)

NEW: Agent Prompt: /simplify slash command — Adds /simplify behavior that runs four cleanup agents for reuse, simplification, efficiency, and altitude findings, then applies safe fixes while skipping behavior-changing or out-of-scope suggestions. NEW: Data: Claude Code live documentation sources — Adds official Claude Code documentation URLs and topic-specific WebFetch prompts for commands, settings, hooks, MCP, skills, subagents, IDEs, deployment, security, and related surfaces. NEW: Data: Claude Code recent changes reference — Adds a reference for renamed or removed Claude Code commands, flags, and terms, including /output-style, /pr-comments, /vim, /extra-usage, --enable-auto-mode, and stale naming guidance. NEW: Skill: Claude Code configuration guide — Adds a Claude Code configuration skill that checks the live build, bundled recent-change references, and current documentation before answering questions about commands, flags, settings, hooks, skills, MCP servers, subagents, IDE integrations, and related configuration. Agent Prompt: Claude guide agent — Adds stale-knowledge handling that tells the guide agent to disclose documentation fetch failures instead of silently answering Claude Code command, flag, or settings questions from memory. Agent Prompt: Security monitor for autonomous agent actions (first part) — Expands security review with explicit final-destination tracing for writes, commits, pushes, uploads, publishes, and sent data before deciding whether a boundary-crossing action should be blocked. Agent Prompt: Security monitor for autonomous agent actions (second part) — Strengthens data-exfiltration rules around trust boundaries, automated pathways, unverified destinations, credential leakage into persistent artifacts, and destination/resource/operation-scoped allow exceptions. Data: Anthropic CLI — Updates Anthropic CLI authentication guidance to cover SDK-style credential resolution, OAuth profiles from ant auth login, ant auth print-credentials, bearer-token usage for raw HTTP, and precedence between API keys and auth tokens. Data: Claude API reference — cURL — Updates examples and adaptive-thinking guidance for Opus 4.8. Data: Claude API reference — Go — Updates the recommended Go SDK model constant and examples from Opus 4.7 to Opus 4.8. Data: Claude API reference — Python — Updates credential guidance for API keys, auth tokens, and ant auth login; adds beta mid-conversation system-message examples; and extends adaptive thinking and compaction guidance to Opus 4.8. Data: Claude API reference — TypeScript — Updates credential guidance for API keys, auth tokens, and ant auth login; adds beta mid-conversation system-message examples; and extends adaptive thinking and compaction guidance to Opus 4.8. Data: Claude model catalog — Adds Claude Opus 4.8 as the current most powerful Opus model with a 1M input window and updates Opus model-selection examples and legacy recommendations to prefer claude-opus-4-8. Data: HTTP error codes reference — Updates authentication fixes for OAuth bearer tokens and expands Opus model-specific 400 guidance to include Opus 4.8. Data: Managed Agents reference — Python — Updates client initialization examples to prefer environment, auth-token, or ant auth login credential resolution before explicit API-key injection. Data: Managed Agents reference — TypeScript — Updates client initialization examples to prefer environment, auth-token, or ant auth login credential resolution before explicit API-key injection. Data: Prompt Caching — Design & Optimization — Adds beta mid-conversation system-message guidance as a cache-preserving and prompt-injection-safe way to send operator instructions without editing the top-level system prompt. Data: Streaming reference — Python — Updates adaptive-thinking examples for Opus 4.8. Data: Streaming reference — TypeScript — Updates adaptive-thinking examples for Opus 4.8. Data: Tool use concepts — Updates adaptive-thinking examples for Opus 4.8. Skill: Agent Design Patterns — Replaces mid-session guidance with beta role: "system" messages for supported models, with retained as the fallback. Skill: Building LLM-powered applications with Claude — Adds Opus 4.8 to current model guidance, updates adaptive thinking, effort, task-budget, compaction, and migration recommendations, and documents beta mid-conversation operator instructions. Skill: Model migration guide — Adds Opus 4.8 migration guidance, including no new API breaking changes from Opus 4.7, model-ID updates, mid-session system prompts, long-horizon agentic tuning, effort recommendations, tool-triggering behavior, narration changes, ask-rate calibration, and visible-reasoning mitigation. System Prompt: Background session instructions — Changes temporary-file guidance from $CLAUDEJOBDIR to $CLAUDEJOBDIR/tmp for background sessions. System Prompt: Coordinator mode orchestration — Updates PR activity subscription guidance and changes worker summary account

Optimizing Memory

I'm currently ingesting all transcripts from calls with clients from my fractional CRO services. I am using claude to store these transcripts and creating memory stores of all information from transcripts. What is the best way to optimize memory and continuously "update" it as new transcripts come in. For example, I have 10 transcripts already with company X. When I have a meeting with them next Monday, I would like the memory store to update with information. Has anyone done something like this? If so, what is the best way to engineer it? submitted by /u/MaybeRemarkable5839 [link] [comments]