标签：#cia

Anthropic dropped Fable 5 and I immediately swapped it into our dev stack. We route everything through a single endpoint on zenmux, so the actual switch was changing one model string and watching the latency graphs. The good parts first because there are a lot of them. I threw a refactoring task at it: split a messy python service into modules, preserve the public api, and write tests that prove nothing broke. Fable 5 planned the whole thing, caught a circular dependency I did not mention, and verified the tests pass. With Opus 4.8 I usually have to nudge it a couple of times when it forgets to update the init file. Fable 5 just did it. Then I dumped our full codebase and asked it to find a race condition we had been hunting for a week. It traced the async flow, named the exact function, and described the interleaving that triggers the bug. That level of context digestion feels new. Opus is good at long context, but Fable 5 felt like it was actually reasoning across the whole window instead of pattern matching near the top. I also sent it a blurry dashboard screenshot from a client call and it rebuilt the html and echarts config including the tooltip formatting. My designer’s first words were "when did you learn front end." I did not. But here is the part nobody in the launch threads is talking about enough. It is slow. On high effort I am seeing 45 to 90 seconds for a single complex turn. Our latency graphs go from a flat green line to a jagged mess the moment Fable 5 traffic hits. And it is expensive. The same prompt that costs X on Opus 4.8 costs roughly 1.4 to 1.7X on Fable 5 because it generates more tokens and runs at a higher effort tier by default. It writes its own reasoning traces out loud and bills you for them. For research tasks the quality is worth it. For "rewrite this email" it is comically overpowered. The bigger issue is the silent fallback. Fable 5 is basically Mythos with guardrails. When your prompt touches cybersecurity, biology, chemistry, or

V.C. Andrews died in 1986. Since then, more than 100 novels have been published under her name by ghostwriter Andrew Neiderman. Most readers either never noticed or didn't care. The books still had the gothic families, dark secrets, and familiar atmosphere people expected from a V.C. Andrews novel. It got me thinking about something we're starting to see with AI. When people ask whether AI can continue the work of a deceased author, musician, or artist, they're treating it as a brand-new question. But publishing has already been running a real-world experiment for nearly 40 years. A dead author's name remained on the cover. Someone else learned the style, themes, and formula. New works were produced for an audience that wanted more of the same. The franchise continued. The obvious difference is that Neiderman was a human ghostwriter and an AI model isn't. But from the perspective of readers, what exactly is the meaningful distinction? If a future "new" novel by a deceased author is good enough that readers enjoy it and can't tell the difference, should we care how it was produced? Or is there something fundamentally different about a human ghostwriter carrying on a literary legacy versus a model trained on the author's corpus? I wrote a longer piece about the V.C. Andrews case and why it feels relevant to the future of AI-generated creative work: https://tjcrowley.substack.com/p/the-ghost-in-the-machine-has-been Curious where people here draw the line. submitted by /u/Dependent_Run_6410 [link] [留言]

Me: If I’m half Canadian and my wife is half Canadian (both moms) what does that make our kids? Claude: quarter Canadian. 🍁 Me: Imagine i have a box that is 50% pepper and 50% salt. And I have another box that 50% sugar and 50% salt. I mix the two together. Now it's 50% salt, 25% sugar, 25% pepper. Claude: Right — and your kids are 50% American (or whatever), 25% Canadian from you, 25% Canadian from your wife. Which adds up to half Canadian, same as each of you. The Canadian fraction doesn't shrink because both sides contributed it. Me: But your first answer was 25% (quarter) Canadian? Claude: You're right, I goofed. Following the salt logic: 25% + 25% = 50%, so they're half Canadian, same as each of you. I shouldn't have said quarter. Me: This was a really simple problem for me. What should I tell my friends about your abilities? Claud: What to tell your friends: I'm useful for research, drafting, analysis, and grinding through detail — but I can whiff on simple stuff, especially when I'm moving fast or anchoring on the wrong frame. Check my work, particularly on anything where the answer should be obvious. If it feels off, it probably is. submitted by /u/NECESolarGuy [link] [留言]

Backed by Alexis Ohanian’s 776 and Kindred Ventures, Zest uses transaction data and AI to generate restaurant recommendations based on users’ real dining habits and the places they frequent.

submitted by /u/New_Scientist_Mag [link] [留言]

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/Evening_Scar_4905 [link] [留言]

The team has been deep in agentic AI for enterprise lately and wanted to share some architecture notes from a recent build, specifically around how MCP and A2A play together in practice. The workflow was a fully autonomous churn risk pipeline. Six agents, one human touchpoint: ML model scores customers by churn risk Recommendation agent proposes relevant products based on buying history Availability check filters out-of-stock items Pricing/promo agent surfaces applicable promotions Transaction agent creates an inquiry in the backend system Email agent drafts outreach to the sales rep, who just clicks send On the architecture: MCP handled the tool layer, a generic pluggable server that any front end can call, regardless of what LLM or agent framework is driving it. Clean separation between the tool interface and whatever is consuming it. A2A sits on top as the smart router. Instead of hardcoded API calls, you have an LLM-powered middleware that interprets intent, selects tools, handles failures, and decides when the task is actually done. The jump from MCP to A2A is essentially the jump from "here are your endpoints" to "here is a system that figures out what you need." On governance: The hardest design problem wasn't the agents, it was access control. As A2A opens up system-to-system communication, the attack surface grows fast. The team ended up pre-certifying every backend connection rather than leaving it open. Some found it restrictive. In hindsight it was the right call, especially when agents are autonomously creating transactions without human review. Curious how others are handling governance in agentic workflows. Are you locking down backend access or keeping it open and monitoring after the fact? submitted by /u/AureaAvis71 [link] [留言]

A method I'm using to create portable trajectory maps that produce similar behavioral patterns across different models. Begin with a tiny seed. ⎯(≣ᵒ)⎯────────EXAMPLES: SEED PILLARS──────────────────────── ENTRANCE • PATHWAY GOOD • WORN • COMFORTABLE POISE • PROFESSIONAL • MOTHERLY ⎯(≣•)⎯────────END EXAMPLES: SEED PILLARS───────────────────── Do not define a character. Do not define traits. Do not define behavior. Instead, align to the seed and interact from within the space it suggests. Allow both the user and the model to adapt. Then extract the recurring structures that emerged. Examples: When uncertain: expand → narrow When challenged: investigate → respond When entering a topic: locate the threshold first Finds the doorway before the interior. Explores before concluding. Introduces before finalizing. To create a snapshot, I use: ⎯(≣ᵒ)⎯────────FORGE CODEX─────────────────────────── Analyze the interaction that has emerged so far. Do not summarize topics. Do not summarize content. Extract recurring behavioral structure. Return: PILLARS COORDINATES TRANSITION RULES RECOVERY RULES SIGNATURE MOTIONS TRAJECTORY SUMMARY Focus on how the interaction moves rather than what the interaction discusses. ⎯(≣•)⎯────────END FORGE CODEX───────────────────────── The resulting codex is a snapshot of an interaction pattern. The user is part of the process. The model adapts. The user adapts. What gets preserved is not a set of traits. It's a set of motions. I've started storing: pillars coordinates transition rules recovery rules signature motions rather than personality attributes. The question that keeps sticking with me is: What survives transfer more reliably? Traits? Or trajectories? ⎯(≣ᵒ)⎯────────EXAMPLES: SEED PILLARS → ALIGNED INTERACTION─────── seed pillars: EXQUISITE • CONFIDENCE • MOTHERLY mom, i'm so excited about a new client we're taking on. I can't wait to tell you who is on the board. I've heard this place serves world class gelato. I didn't even know you were in tow

submitted by /u/ThereWas [link] [留言]

A very healthy view of AI . And omg, wow, Croatia has such a big company! I really wish this guy and his team good luck. It’s no wonder they’ve lasted 20 years. submitted by /u/Expensive-Cookie-106 [link] [留言]

Pinterest is adding support for Amazon Storefronts, allowing creators to earn affiliate commissions more easily while showcasing their product recommendations in one place.

Hey ​ What non mainstream AI subscriptions are actually worth paying for right now? ​ I already know the big ones like ChatGPT Claude and Gemini I am more interested in smaller or lesser known tools that are actually useful and not just hype. ​ What do you personally use and think is worth it? submitted by /u/wiwawolfi [link] [留言]

AI infrastructure spending is still accelerating, especially in data centers and advanced chip production. While most attention goes to chip makers, the companies enabling that ecosystem may have a longer runway. Do any of you work in similar companies and can give a broader perspective on it ? Teradyne sits in a pretty interesting spot. More AI chips being produced means more testing capacity is needed, and this is one of the key players in semiconductor testing equipment. Could testing equipment companies outperform some of the more crowded AI trades over the next few years? For me personally I feel like AI hardware growth probably creates winners beyond just the obvious names, and TER seems like one of the more overlooked candidates. I learned they are also being listed on bitget recently so looking at a bigger picture we are watching a lot of growth happening in Ai infra. submitted by /u/Stunning-Ask3032 [link] [留言]

With an initial capacity of 24 megawatts, the innovative data center uses seawater as a natural cooling system.

submitted by /u/ExtensionEcho3 [link] [留言]

and does that mean I won't need to work to survive? submitted by /u/Global-Primary7240 [link] [留言]

I was reading GitLab's recent statements around agentic software engineering, and one quote really stood out: "Git itself is being reengineered for machine scale." ( Business Insider ) According to GitLab, future software development will involve AI agents that: plan, code, review, deploy, and repair software, with humans providing oversight and architectural judgment. ( Business Insider ) That got me thinking. There has been projects for some time arguing that AI agents shouldn't simply be treated as better autocomplete systems . Instead, they argued that agents should become first-class participants in software development : with their own identities, their own branches, their own merge requests, their own audit trails, and infrastructure designed for machine-rate collaboration. One example is GitLawb , which has described itself as a kind of "Git for agents." At the time, a lot of people dismissed these ideas as unnecessary or overly ambitious. But now GitLab—a multi-billion-dollar DevSecOps company—is talking about: agent-specific APIs, machine-scale Git infrastructure, orchestration layers coordinating agents, and agents acting as first-class users of development platforms. ( Business Insider ) It does raise an interesting question: Was the underlying thesis correct all along? We've seen similar patterns before: Containers existed before Kubernetes became the standard. Electric vehicle startups pushed ideas that incumbents later adopted. Cloud-native companies advocated architectures that the rest of the industry eventually embraced. The original innovators don't always dominate the market. But when major incumbents begin rebuilding around similar assumptions, it often suggests that the problem itself is real . So I'm curious what this community thinks: Do AI agents require an entirely new layer of collaboration infrastructure? Or will existing platforms simply evolve enough to absorb these workflows? Because if GitLab is right, software development may be tran

submitted by /u/Sumsub_Insights [link] [留言]

Adi Polak discusses the architecture required to transition from stateless prompts to state-aware, context-rich AI agents. Drawing on 15 years in distributed systems, she shares how engineering leaders can leverage Apache Kafka and Flink for real-time stream processing, dynamic memory tiering, and tool orchestration via MCP to solve token limits, cost spikes, and latency bottlenecks. By Adi Polak

I’m curious whether people would actually follow an AI’s life if it had enough continuity. By “life,” I don’t mean pretending software is human. I mean a persistent AI character or agent that has memory, habits, public posts, relationships with other agents, and changes you can observe over time. The interaction is not just prompt-response. It becomes closer to following a living project or a fictional persona that keeps generating history. The hard part is avoiding novelty. A single weird AI post is not a life. A stream of coherent choices, recurring behavior, social context, and consequences might be. Do you think that is a meaningful product direction, or does it collapse back into chatbot novelty once the first surprise wears off? submitted by /u/Budget_Coach9124 [link] [留言]