标签：#artificial

[ 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]

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]

submitted by /u/fxboshop [link] [留言]

Text based, I don't want any cheesy porn AIs please 😅 submitted by /u/holupIgotthis [link] [留言]

AI is already deciding who gets loans, who gets job interviews, who gets flagged for benefits fraud. Not assisting humans in making those decisions. Making them. And in most countries there is no law requiring anyone to tell you AI was involved, explain why it decided what it did, or give you any way to challenge it. That needs to change. We need laws that say if an AI makes a decision about you, you have the right to know, the right to understand why, and the right to challenge it. A human must always be accountable for the outcome. That’s not anti-innovation. That’s just basic protection for people living in a world already being shaped by these systems. Most governments don’t understand it well enough to even write those laws yet. Most politicians making AI policy genuinely cannot explain how these systems work, who owns them, or what accountability looks like when they go wrong. Voluntary frameworks have failed every single time. Social media companies voluntarily committed to reducing harm. They didn’t. Financial firms voluntarily committed to responsible lending. They didn’t. Voluntary always means the least responsible actor sets the standard. Hard law is the only mechanism that has ever reliably produced accountability at scale. We need it for AI before the damage is done — not after. The window to get this right is still open. But it won’t stay open forever. submitted by /u/United-Actuator-3527 [link] [留言]

Something worth paying attention to in the Fable 5 launch that I think will get buried under benchmark comparisons. The most consequential line in the AWS announcement wasn’t about context windows or coding performance, it was tucked into the infrastructure section: “Once you opt into data retention, your data will leave AWS’s data and security boundary.” That’s not a model feature, that’s an enterprise architecture constraint. For a lot of companies that sentence alone disqualifies Fable 5 from touching certain workloads no matter how good the model is. The Fable vs Mythos split is also worth sitting with. Same underlying capability apparently, but Mythos is gated behind Project Glasswing and vetted partners only. Anthropic is essentially saying some capability is too sensitive for flat API access, which is a pretty different philosophy than “here’s our best model, go build.” Does the Fable/Mythos split read as responsible deployment to people here or more like managed scarcity? And anyone in enterprise AI already hitting the retention requirement as an actual blocker? submitted by /u/Old_Cap4710 [link] [留言]

It kinda creeps me out. Firstly it started from like on chinese word in my chatgpt, now it's fully in chinese? submitted by /u/Oldrus [link] [留言]

hey everyone, sharing something i think will be genuinely useful for anyone building with AI agents. most agent failures aren't caused by the model — they're caused by poor evaluation. agents that work in demos but fail in production, tool calling workflows that silently break, prompt updates that introduce regressions. teams discover these problems only after deployment when it's already too late. we're hosting the Agent Evals Bootcamp on June 27 with Ammar Mohanna, PhD, an AI engineer, researcher and expert in production AI and agent evaluation. 5 hours live, hands on throughout. you work through real evaluation scenarios across 4 layers — component evaluation, trajectory evaluation, outcome evaluation and adversarial evaluation. what every attendee gets: practical evaluation framework you can apply immediately 6 months access to an AI Evals assistant hands on exercises and implementation templates capstone project completed on the day Packt endorsed certification for your LinkedIn link in first comment submitted by /u/Plenty-Pie-9084 [link] [留言]