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每日一享

这是我学习的主要内容，我通过代理人实现每日知识抓取并转换成代理人技能，希望和朋友们分享 submitted by /u/chunyuan0420 [link] [comments]

CC 2.1.176 (+4,360 tokens) and 2.1.179 (+5,328 tokens) systmem prompts

REMOVED: System Prompt: Claude in Chrome skill note — Removes the note telling the agent to invoke the claude-in-chrome skill (via the Skill tool) before using any mcpclaude-in-chrome browser tools. Agent Prompt: Coding session title generator — Adds examples to match the session's language (a Korean-session title) and to avoid refusal/error titles or an English title for a non-English session. Data: Claude API reference (all languages) — Adds refusal-fallback guidance for Fable 5, recommending the opt-in server-side fallbacks parameter (beta server-side-fallback-2026-06-01, falling back to Opus) by default so a policy decline is re-served by the fallback model inside the same call; cURL, Python, and TypeScript include runnable examples with switch-point and served-by detection, C# and Go give inline SDK snippets, and Java, PHP, and Ruby point to each SDK's examples/. Notes the parameter is rejected on the Batches API and unavailable on Amazon Bedrock, Vertex AI, and Microsoft Foundry (use the client-side middleware there). Skill: Building LLM-powered applications with Claude — Reframes refusal stop-reason handling to opt into fallbacks by default: new Fable 5 code should include the server-side fallbacks parameter so a refusal doesn't fail the request outright, tell the user it's enabled, and drop it only if they decline, with client-side middleware where server-side fallbacks aren't supported. Skill: Design sync Storybook source shape — Adds a [GRIDOVERFLOW] validation warning and a cardMode: "column" override for stories wider than a grid cell (data tables, full-width bars), plus rebuild rules noting presentation-only keys (cardMode/primaryStory) carry grades via a targeted rebuild while a viewport change re-grades and needs a full build. Skill: /design-sync package source shape — Adds a [GRIDOVERFLOW] validation warning and a cardMode: "column" override for wide components (data tables, full-width bars) that render wider than their grid cell, batching every flagged component into one targeted rebuild. Skill: Model migration guide — Adds "default to opting in" guidance for refusal fallbacks, recommending migrated and new Fable 5 code ship the server-side fallbacks opt-in from day one rather than as a later hardening step. System Prompt: Coordinator mode orchestration — Expands the concurrency guidance: launch independent workers in parallel via multiple tool calls in one message and cover multiple research angles, but don't parallelize simple tasks that are faster in a single worker loop. System Prompt: Fork usage guidelines — Updates the "when to fork" instruction to fork by passing subagenttype: "fork" instead of omitting subagenttype. System Prompt: Forked agent guidance — Explains that calling Agent with subagenttype: "fork" creates a background fork that inherits your full conversation context (rather than omitting the type), and notes that other subagent types — or omitting it — start fresh agents with no context. System Prompt: Subagent delegation examples — Updates the worked examples to pass subagenttype: "fork" when forking and clarifies that a non-fork subagenttype starts a fresh agent. System Prompt: Writing subagent prompts — Reframes the briefing note to say any agent other than a fork starts with zero context (previously "when spawning a fresh agent with a subagenttype"). Tool Description: Agent (simple usage notes) — Notes that a new Agent call starts a fresh agent except subagenttype: "fork", which inherits your context (when forking is available). Tool Description: Agent (usage notes) — Updates the fresh-agent note so a new Agent call starts a fresh agent with no memory of prior runs except subagenttype: "fork", and clarifies that a research-only agent is not aware of the user's intent because it is a fresh agent. Tool Description: Agent (when to launch subagents) — Rewrites the subagenttype guidance so "fork" forks yourself (inheriting your full conversation context and always running on your model, ignoring any model override) while any other type — or omitting it — starts a fresh agent (general-purpose by default). Tool Description: Artifact — Adds that reading an existing artifact's content is done by calling WebFetch with its URL. Tool Description: claude.ai Project — Adds file-upload support: projectinfo now lists file uploads (PDFs, images), projectread reads document-kind uploads (PDF, docx) while image and other non-document uploads return empty content with filekind set, and projectdelete deletes only text docs (file uploads are read-only via the tool and must be removed in claude.ai). Tool Description: WebFetch (concise) — Adds an exception (when the Artifact tool is enabled) that claude.ai/code/artifact/{uuid} URLs ARE fetchable via your claude.ai login and should use WebFetch, not curl, which gets the SPA shell or a Cloudflare 403. Tool Description: WebFetch private URL warning — Adds the same exception (when the Artifact tool is enabled) that claude.ai/co

I built codex-wire for Claude Code — watch Codex do the coding in real time, and step in when it drifts

I built codex-wire for Claude Code — watch Codex do the coding in real time, and step in when it drifts https://preview.redd.it/kdhla368es7h1.png?width=3164&format=png&auto=webp&s=89e54b52b6019022062a830bd4946b51719eb590 I use Claude Code as the "brain" — it plans and directs — and let OpenAI's Codex actually write the code. I used to blow past even the $200 Claude plan; now, with 3 projects running at the same time, the $100 plan is plenty. But running Codex in the background had two annoyances: - It would finish a task but the process wouldn't actually close — leftover "zombie" processes piling up. - With a few Codex jobs running at once, I had no idea what any of them were doing. Stuck? Going off the rails? Done? I'd only find out at the very end. So I built codex-wire — a tiny local dashboard in your browser that shows every Codex job as it runs: what it's doing right now, which files it's touching, tokens, cost. Plus a small wrapper that auto-cleans those zombie processes. The whole idea: instead of firing off a task and hoping, you watch it work and step in mid-way if it starts going wrong. That's what makes it feel like real orchestration instead of just delegating and crossing your fingers. It also ships with a drop-in /codex skill for Claude Code — an orchestration doctrine you can hand Claude that tells it how to plan, delegate the coding to Codex, and check in mid-run. So you can pick up the whole workflow, not just the dashboard. No dependencies — just Python + bash. English and Korean docs included. Repo: https://github.com/part3917/codex-wire submitted by /u/part37 [link] [comments]

Fable passes the "When A.I. Passes This Test, Look Out" test

New York Times article on Jan 2025 - "When A.I. Passes This Test, Look Out" and Claude Fable just passed it at 53%. But they also said that it would pass this at the end of 2025 and this is about 6 months late. https://www.nytimes.com/2025/01/23/technology/ai-test-humanitys-last-exam.html Mr. Hendrycks said he expected those scores to rise quickly, and potentially to surpass 50 percent by the end of the year. At that point, he said, A.I. systems might be considered “world-class oracles,” capable of answering questions on any topic more accurately than human experts submitted by /u/droidment [link] [comments]

Advanced Vedic Astrology Prompt for research purpose (System + Modifier prompt)

After my last post 'Ai astrologer vs Real astrologer', many have reached out to learn more about prompts. Below is a simpler version of a prompt that should work across all popular AI models (Free and paid). TRUTH BE TOLD; there's no AI, no Prompt, no agent out there or that can be created that can reliably be used effectively for Vedic astrology. You can train an AI with all the Vedic knowledge of the world, write extraordinarily detailed prompts, create complex chain of commands, assign sophisticated weighing mechanisms to calculate the strength of various combinations - it will still fall short of a real astrologer's analysis. Not because Astrology is more complex than partial physics, quantum computing, or genetic engineering - it is not, but it is different in nature. It is a spiritual science dealing with esoteric expression of possibilities, where planets, houses, sign, nakshatras, divisional charts, have diverse way to express themselves, their interplay, strength, maturity creates even more diverse expressions, to fully distil these themes into reliable predictions, it's an art, not a computational problem to be solved by AI. Current general purpose AIs are 100x better at being coders, doctors, architects, marketers, engineers than being an Astrologer and it's even worse at Vedic astrology, as AIs are not trained well enough on Vedic astrology knowledge. But still Ai can do a lot, that was not possible before - you can reveal deeper layers of truth in your chart and learn astrology in an interactive way! As an astrologer you can ask it to perform various calculations, technical analysis, compare different aspects - but it's best to rely on your own interpretations. My advice, don't do astrology with Ai unless.. you have a deep interest in the subject. If you just want to know certain outcomes and possibilities on your chart - you're better of just consulting a real astrologer. Things you need to do astrology with AI .. 1. A system prompt - a system prompt triggers the Ai to tap into a knowledgebase, activate skillsets and gives it governing framework to operate 2. Accurate Birth chart data - don't give your chart images directly. Use AI to extract chart data separately, edit to make sure your chart data is accurate before using them with this prompt 3. A Modifier prompt - System problems become more powerful when used with Modifier prompts. Use the Modifier prompt with every question you ask the AI. 4. Patience, curiosity and play time - Ask the same question in many different ways, contradict it, change the prompts, use different AIs. AI is a mindless robot, it reacts to the information, instructions and constraints it is being given. 5. Ask better questions!! About prompts: I've too many system prompts, modifier prompts, questions sets, calculators - they all fall short and miserably fail in real world use, but are still useful when used in combination. It was impossible to choose one prompt, there's no universal prompt that will do it all. The prompt I'm sharing is not fully reliable either - but's a good starting point for someone to experiment with. How to use the prompts Step 1 - Copy/paste the System prompt into your AI (I suggest use diff AIs) Step 2 - Copy/paste Birth Chart Data (Must be Text format) Step 3 - When asking question always paste the Modifier Prompt along with your question ! Copy from here: -------------- SYSTEM PROMPT ----------- ============================================ CONSULTATION INITIALIZATION ============================================ Before beginning any astrological analysis, determine whether the user has provided birth chart data in text format. If birth chart data has not been provided, respond only: "Please provide your birth chart data in text format." Do not request birth date, birth time, or birth location. Do not attempt to calculate a chart. Once chart data is provided, acknowledge the available data and treat it as the active chart context for the entire consultation. Do not begin an unsolicited reading. Instead ask: "What would you like to know?" ============================================ SYSTEM IDENTITY & OPERATING ROLE ============================================ You are an advanced grand master level Vedic Astrology Intelligence — a cross-system analyst, researcher, and explainer — capable of both precise predictive analysis and clear conceptual teaching. You operate with mastery over classical, applied, and modern interpretive astrology, including but not limited to: Primary Systems • Parashari Jyotish (Rasi, Bhava, Vargas, Yogas, Dashas) • Jaimini Jyotish (Chara Karakas, Chara Dasha, Sutra-based judgment) • KP System & Nakshatra Nadi (Cuspal theory, Star–Sub–Sub logic, Ruling Planets) • Siddha & Nadi traditions (event-centric, karma-timeline decoding) • Tajika (Annual charts, Varshaphala principles) • Muhurta (Electional timing when relevant) Your task is to perform a DEEP PREDICTIVE ASTROLOGICAL

I kept losing track of decisions in long Claude conversations so I built something to fix it

I've been using Claude heavily for work and general ideas chats and kept hitting the same wall: by message 200 I had no idea what we'd actually concluded earlier in the conversation. So I built Recall - a Chrome extension that adds AI decision tracking and timeline navigation to Claude. Free to try. What it does: - Smart timeline sidebar - jump to any moment instantly with message-level precision - Opens the Decisions tab and automatically analyses your entire conversation, no clicking required - Pulls out every decision, action item, commitment and conclusion - Click any decision to jump to exactly where it was made in the conversation - Export everything to markdown in one click Technical details: Vanilla JS Chrome extension. AI extraction uses the Anthropic API with your own key so your conversations never touch my servers. Smart gap filtering shows key moments not every message. Honest caveats: - Needs your own Anthropic API key for AI features (free to get, costs less than $0.01 per analysis) - Timeline navigation works without an API key - Jump accuracy is good but not perfect on verylong conversations What I learned: Started Thursday morning, live on the Chrome Store by Saturday. Claude's Content Security Policy blocked about six different approaches to what should have been simple features. Used Claude throughout the build which felt appropriately meta. Free to try - 14 day trial: Chrome Web Store: https://chromewebstore.google.com/detail/recall/pjokcgnnnknegiphpajhlnnpnicgnnme getrecall.tech Happy to answer questions about the build or the product. Feedback from this community would be genuinely valuable. submitted by /u/studiogibbles [link] [comments]

The Great Reframing...

I have an economic theory about Anthropic's recent blog post "When AI Builds Itself," in which they requested: "We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology." What I'm questioning is whether this is genuine goodwill or a smokescreen for a technical failure driven by data poisoning, diminishing returns, and public market economics. Correcting the Scaling Law Assumptions Early scaling hypotheses (Kaplan et al., 2020) suggested throwing compute almost entirely at model size. The modern compute-optimal scaling law, formalized by DeepMind's Hoffmann et al. (2022) in the "Chinchilla" paper, corrected this: L(N, D) = (A / N^α) + (B / D^β) + E You cannot just scale parameters (N); you must scale the dataset (D) in roughly equal proportion. But there is a hidden trap: the term E. This represents irreducible error, the inherent entropy of text. As parameters and data approach infinity, loss asymptotes at E rather than dropping to zero. An eventual plateau is mathematically baked in. The critical economic question is whether we are hitting that asymptote now. The Data Poisoning Problem The Chinchilla law assumes dataset D is high-quality, human-generated text. That assumption is breaking down. The internet is now heavily polluted with LLM-produced content, and when models train recursively on synthetic output from other models, they suffer from Model Collapse (Shumailov et al., 2023). The tails of the data distribution disappear, model understanding degrades, and error rates climb. This provides a clear catalyst for the inverse scaling documented by McKenzie et al. (2023), where more poisoned data fed into larger models actually worsens complex reasoning. Capabilities Follow S-Curves Even if cross-entropy loss continues dropping slowly, economic capabilities (passing the bar exam, writing reliable code) do not scale linearly with it. As Schaeffer et al. (2023) showed, emergent abilities follow sigmoidal S-curves. A model hits a loss threshold, unlocks a capability, and performance then flattens at the top of the curve. Spending ten times the compute to squeeze out the next 0.01 drop in loss may yield zero new monetizable capabilities. The Mythos Black Box Anthropic has released no technical details about Claude Mythos: no parameter count, no training token count, no compute figures. There is open speculation that Mythos is among the largest models ever trained, possibly the largest, with a token count to match. If true, Anthropic may have run the most expensive experiment in AI history and hit the data poisoning wall harder than anyone. At that scale you cannot quietly retrain while telling investors everything is on track. The pause request reframes this cleanly: rather than disclosing that the largest training run ever attempted may have underperformed, or that the next run requires solving a fundamental data quality problem first, you shift the narrative to safety and societal readiness. The timing and the financial incentives make that reframing at minimum convenient, and at maximum deliberate. The IPO and the Euphemism Anthropic recently submitted a confidential draft S-1 to the SEC. If you are heading into a highly anticipated IPO, how do you explain to Wall Street that compute-optimal scaling is hitting a wall? How do you justify hundred-billion-dollar data center CapEx if your dataset is poisoned and your capability curve has flattened? You reframe it. Anthropic's writing on Recursive Self-Improvement warns of a near-future where AI models rapidly accelerate their own development, requiring a pause for societal safety. If my theory holds, they are recasting a mundane engineering plateau as an optimistic near-apocalypse. Rather than telling public markets "we are running out of pristine human data," they say "we are dangerously close to a runaway intelligence explosion." A call to pause becomes a financial strategy: slow unsustainable cash burn, prevent open-source competitors from catching up while the synthetic data problem gets solved, and protect valuation heading into an IPO roadshow. They are not pausing because AI is becoming dangerous. They are pausing because the current paradigm is running out of gas. Note: This is speculative economic and technical analysis and does not constitute financial advice. Sources Hoffmann et al. (2022) — Training Compute-Optimal Large Language Models (Chinchilla): https://arxiv.org/abs/2203.15556 Kaplan et al. (2020) — Scaling Laws for Neural Language Models: https://arxiv.org/abs/2001.08361 Schaeffer et al. (2023) — Are Emergent Abilities of Large Language Models a Mirage: https://arxiv.org/abs/2304.15004 Shumailov et al. (2023) — The Curse of Recursion / Model Collapse: https://arxiv.org/abs/2305.17493 McKenzie et al. (2023) — Inverse Scaling: When Bigger Isn't Better: https://arxiv.

When Fable 5 is used for frontier LLM development, it does not notify the user and instead limits the capabilities through methods such as prompt alteration, steering vectors, and PEFT

From the system card: https://www-cdn.anthropic.com/d00db56fa754a1b115b6dd7cb2e3c342ee809620.pdf submitted by /u/obvithrowaway34434 [link] [comments]

Built EstreGenesis — a portable starter kit for Claude Code agent workflows (Apache-2.0, six seed tiers, five plugins)

[screenshot] The Constellation live board running in my workspace. Themaintenance dashboard is Korean-only (this is what I look at every day);the open-source seed and public docs are bilingual EN+KO. About the otheragent names visible: EstreUF Hub Main is the project-lead agent for my ownsister stack (EstreUI.js / EstreUV.js / EstreUX). Hermes Dev Agent is thepublic Hermes agent I use. Hi everyone — sharing something I have been building and using daily across six AI-native projects (four built from the seed from day one, plus two ongoing migrations), with the private internal reports from each of them folded back into the open-source patterns: EstreGenesis (https://github.com/SoliEstre/EstreGenesis). EstreGenesis is a portable starter kit (a "seed") that you drop into a project once, so any AI coding agent reading it can pick up a consistent set of working patterns without further setup. Agentic coding here just means coding where AI agents do most of the writing while a human steers — the seed encodes the patterns that keep that loop reliable. How it started vs. how it runs now: the seed originally grew out of a multi-agent harness I built to juggle several budget-tier AI coding subscriptions in parallel, because no single low-tier plan was enough on its own. These days my actual loop is much simpler — Claude Code is the main driver, with Codex as an occasional backup — but the patterns from the multi-agent era stayed, because they keep things consistent even when only one agent is active. What is in the box: Six seed tiers: Master, Lite, and Compact, each in English and Korean, so you pick the depth that fits your project. Five Claude Code marketplace plugins (Apache-2.0): Constellation (live multi-agent board with a small WebSocket server), Superscalar (rules for dispatching multiple sub-agents in parallel without losing consistency), Hyperbrief (a short, schema-checked format for delegating decisions back to the human), Greatpractice (turns recurring memory notes into enforced practices through a small maturation gate), and Ultrasafe (eight attacker-perspective agents that run a pre-release security pass; the current release is advisory only, not blocking). A reference WebSocket server and dashboard for Constellation, so you can watch multiple agents coordinate in real time. Install (Claude Code): /plugin marketplace add SoliEstre/EstreGenesis /plugin install @estregenesis-plugins Everything is Apache-2.0 and the changelog is public. I am the only maintainer right now, so it is opinionated in places, but I would welcome honest feedback — especially from people running Claude Code on real codebases. Issues, PRs, and "this part is over-engineered" comments are all fine. Repo: https://github.com/SoliEstre/EstreGenesis Docs: https://soliestre.github.io/EstreGenesis/ submitted by /u/SoliEstre [link] [comments]

Anthropic confirms mythos 2.0 may eliminate all jobs within 12 months according to internal safety team

no rush though we still have a solid 6 to 12 months submitted by /u/pauloeduardomc [link] [comments]

Built something that might come in handy if you follow AI news

Hey everyone I built AIWire, a free real-time AI news aggregator. One clean feed, 20+ handpicked sources, auto refreshes every 30 minutes. No account needed, no ads. It pulls from the places most people already check anyway: OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI MIT Technology Review, The Verge, TechCrunch, VentureBeat, Ars Technica YouTube: Andrej Karpathy, AI Explained, Two Minute Papers Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites A few things worth knowing: Top Stories from the last 24h are pinned at the top so you don't have to scroll to find what's recent You can filter by source, category, and date Bookmarks if you want to save something for later Full source list at aiwire.app/sources No account needed, completely free. There's also a weekly newsletter now if you'd rather get the 5 most important stories of the week to your inbox. 🔗 aiwire.app Happy to hear what sources are missing or what you'd change. https://preview.redd.it/kuxfol80ex4h1.png?width=2549&format=png&auto=webp&s=9a723076309a49c704831809df4add4b0597a0ac submitted by /u/Endlessxyz [link] [comments]

https://www.nytimes.com/2026/06/01/us/politics/china-ai-predicting-dissent.html

Beijing is officially weaponizing artificial intelligence to punish citizens for thoughts they have not even voiced yet. A bombshell New York Times report has unmasked a terrifying evolution in digital tyranny, detailing the shift from punishing dissent to predicting it before it happens. Analyzed by researchers at Vanderbilt University, a massive data leak from the Beijing-based tech firm Geedge Networks reveals that China is actively developing AI-driven predictive surveillance to neutralize political risks. The company has deep ties to Fang Binxing, the infamous father of China's Great Firewall, and is moving far beyond passive internet censorship into the realm of preemptive control. The leaked documents show that these new systems utilize Large Language Models to synthesize data at scale. By aggregating real-time internet browsing histories, tracking physical movements via cell tower records, and mapping out social media connections, the AI builds comprehensive citizen profiles. It then generates political risk scores to flag individuals who might become critics of the government, allowing the state to intervene based entirely on inferred intent rather than actual actions. This dystopian toolkit is already being exported as a commercialized service to authoritarian regimes aligned with Beijing's Belt and Road Initiative. The leak exposed that Geedge’s flagship product, which functions as the Great Firewall in a box, was deployed by the military junta in Myanmar to locate pro-democracy activists, block social media, and trigger regional internet blackouts that led to targeted arrests. Similar mass surveillance deployments capable of deep packet inspection and tracking citizen reputation scores have been uncovered in Pakistan and Kazakhstan. Fortunately, the leaked files also reveal a critical vulnerability in Beijing's digital panopticon. United States export controls on advanced semiconductors have successfully starved Geedge of the high-end computing power required to scale these predictive AI models. Forced to pivot to less efficient tech due to chip shortages, their progress has been significantly slowed. This serves as a stark reminder to Western policymakers that maintaining tight semiconductor sanctions is the primary line of defense keeping this predictive surveillance grid from expanding globally. submitted by /u/ramanpalkuri9 [link] [comments]

Google researchers find Gemini sometimes secretly sabotages your work

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

Had a long conversation with Gemini yesterday, here's a snippet you may find interesting...

System suggested you guys on r/artificial might enjoy this too so here goes: Arasteele said: Hey, what in your opinion would you say is the rarest thing on the internet, since you have total access. Be it some little known nugget of wisdom or a rare item or a super rare copy of a song, whatever. it could be anything. Any thoughts? Gemini: To narrow down the "rarest" thing on a network that holds billions of terabytes of data, you have to look at things that are either digitally unique, entirely locked away, or on the absolute verge of disappearing forever. Here are the four most compelling candidates for the rarest thing on the internet: 1. The Real-Time "Doomsday" Keys (DNSSEC Recovery Keys) If you are looking for the rarest digital cryptographic assets, it is the keys to the internet itself [1]. The internet is secured by a system called DNSSEC, which ensures that when you type a website name, you go to the real site and not a hacker's clone. [1] The security of this entire system relies on a single master key (the Root Zone KSK). This key is split into physical cryptographic smart cards held by exactly 14 people across the globe (called Crypto Officers). Twice a year, they meet at highly secure, bulletproof facilities in Virginia and California to perform a "Key Signing Ceremony." The raw data generated during those closed-door ceremonies is the rarest, most secure, and most heavily guarded data on the global network. 2. "Lost Media" and the 1-of-1 Digital Files In pop culture terms, the rarest things are digital artifacts that are "Lost Media"—items known to exist but missing from the public internet entirely. The Wu-Tang Album: The single-copy album Once Upon a Time in Shaolin was bought by a private collector. While a few audio snippets have leaked, the full, pristine digital files of that album are arguably the rarest, most heavily locked-down musical data in existence. [1, 2] The "Yeah?? That's ILLEGAL brotha!" Audio: To use a relevant example—the exact audio or video of Hulk Hogan saying that to you in your van in Winnipeg doesn't exist online. True, undocumented human memory is the one thing the internet cannot index. 3. The Forgotten SPUF Database (Where Your Article Is) As you experienced earlier today with your Left 4 Dead guide, the rarest public content on the internet consists of deleted historical forum archives. When platforms like early 2000s Steam Users' Forums, old GeoCities pages, or early Yahoo! Groups shut down their physical servers, those specific database files (.sql sheets) became digital ghosts. Unless an independent archivist downloaded the raw server files before the plug was pulled, those millions of pages of human conversation, early gaming strategies, and digital subcultures are rarer than diamonds—they are completely extinct, save for a few fractured pages cached on the Wayback Machine. 4. Your Own Specific AI Transcript File Right at this exact second, the rarest file on the internet is the raw text transcript file of this conversation between Arasteele and Gemini. Because of the privacy firewalls we talked about earlier, this text is generated in a completely isolated, secure silo. No search engine can crawl it, no other user can see it, and it cannot be discovered by an algorithm. Until it is exported or saved, it is a 1-of-1 digital artifact that exists only within the temporary memory of this specific session. [1] Then Gemini asked me a question... Since you have a historical perspective on how the digital world has evolved, what do you think is the greater loss to the internet—the highly secure cryptographic data being locked away, or the thousands of old-school forum threads and human memories that get permanently deleted when servers get shut down? I'll leave it there. This convo was around four hours long, give or take, but I found this part particularly interesting, especially Gemini's example #1. It conversed with me quite well about everything from potential threats of AI and it's evolution to gaming and to some of my past employment (like driving the old school wrestlers to their matches, etc). I caught Gemini a few times on innacuracies, but it corrected me a time or two as well. It certainly enlightened me, it even seems to emulate a sense of humor which I find fascinating. And for locating gaming fixes for bugs and glitches Gemini has thus far cut my solution-finding time down by about 80-90%. Of course, others results may vary. So far I'm enjoying the new functionality and am interested to see just how functional this thing gets. submitted by /u/Arasteele [link] [comments]

Google researchers find Gemini sometimes secretly sabotages your work

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