Are we getting back to normal and will we see the bubble pop as we move more towards robotics and hardware? IoT is going to be a thing I guess moving forward. I really think LLMs are dead no more advancements at least for the time being. What does it mean for the markets and for the AI companies though? Who will survive the purge?

The fed can't raise rates without hitting rock bottom and a hardcore recession ( which some argue we are already in,) but if they lower rates inflation is the issue. How the hell did we get here and is 'we do nothing with rates' the way moving forward? What a mess, don't you think? We can't keep it as is either because jobs are dying and the economy is suffering. I have way more to say on this but I'll stop here lol

Nvidia can deliver GPUs in months. A new grid connection for a data center takes a median of ~5 years (per Lawrence Berkeley National Lab). The chips depreciate while the paperwork sits.

Musk lived this firsthand. xAI's Memphis cluster ran 100K GPUs on leased mobile turbines while waiting for permanent power (Lawsuits, DOJ intervention). It was fragile, expensive, and his only option.

Last week he reportedly bought APR Energy, a Jacksonville-based fleet of 1+ GW of mobile gas turbines that deploy in weeks. Dispatchable power he can park anywhere. 1 GW runs about 600,000 H100s.

He was renting his most important input. So he bought the landlord.

Every AI lab can order the same chips. Only one now owns a power fleet that ships by truck and bypasses the queue.

I wrote up a full breakdown on my Substack it's just too long to post here.

Hey folks, I’m Sharon Jerman – AI strategist, developer, and the person behind those “build-your-own agent” workflows you may have seen around Maven and LinkedIn.

Why this subreddit?
I needed a single spot to document the exact playbooks, templates, and experiments I use when helping teams ship custom AI agents. Expect actionable breakdowns, not hype.

What you’ll find here (and in what order):

1. Quick-win guides – snackable posts on scoping, tool choices, and guardrails.
2. Case studies & teardown threads – anonymized but metric-driven.
3. AMAs and office hours – space to troubleshoot your own builds.
4. Live session invites & recaps – so you can join real-time builds or catch the notes later.

What I’d love from you:

• Drop a comment with the #1 workflow you wish an AI agent could handle today.
• If you’ve already built one, share the stack and what you’d change next time.
• Lurkers welcome, but upvotes + feedback help me prioritize what to post next.

Thanks for jumping in early.

https://preview.redd.it/37tor3olyqcg1.png?width=691&format=png&auto=webp&s=81d4758d3efb5ea0f759c6bfc4d5e1554281d096

Functions are reusable blocks of code that take inputs, perform a specific task, and return outputs. In AI and cloud contexts, they act as callable tools that let agents or applications execute precise actions, like querying data or triggering workflows, without managing underlying infrastructure.

Example Flow

User: "What's the weather in Seattle and book me a flight there?"

1. Agent thinks: "I need weather data and flight booking capability"
2. Calls function: getWeather(city="Seattle") → Returns "Rainy, 55°F"
3. Calls function: searchFlights(destination="Seattle") → Returns flight options
4. Calls function: bookFlight(flightId="ABC123") → Confirms booking
5. Agent responds: "It's rainy and 55°F in Seattle. I've booked you on flight ABC123..."

Anthropic began enforcing its terms by restricting Claude Pro and Max accounts (costing $100 to $200 monthly) to its official terminal-based AI coding tool, Claude Code. Users trying third-party apps now get an error message, ending months of smooth integrations that offered features like multi-model support and faster interfaces. The developer community split, with some canceling subscriptions over the 'downgrade' to Anthropic's CLI, while others see it as fair protection of subsidized rates tied to data training. Workarounds popped up fast, but competitors like Amp are already luring users with free Opus access. https://piunikaweb.com/2026/01/09/anthropic-reportedly-blocking-non-official-claude-api-connections/

I just finished watching the new comprehensive course on Agentic AI Workflows (specifically the one by Rola Dali/freeCodeCamp), and I wanted to break down the key insights for the community here.

If you are building local agents, the distinction between Static Workflows and Dynamic Agentic Systems discussed here is crucial. Here is a summary of the concepts and the timeline if you want to jump to specific sections.

Key Takeaways for Builders:

1. Agents vs. Workflows: The video makes a strong distinction. Workflows are static paths (if X, then Y). Agents use LLMs as a reasoning brain to decompose tasks at runtime.
2. Architectural Patterns: It covers the Supervisor vs. Swarm debate.
   • Supervisor: Centralized decision-making.
   • Swarm: Decentralized, hand-offs between agents.
3. The Tech Stack: It dives into LangChain, MCP (Model Context Protocol), and how to handle intrinsic vs. long-term memory.

Video Timeline & Cheat Sheet:

Part 1: Foundations & Theory

• 0:14:41 - Defining Agency: The spectrum of autonomy.
• 0:20:31 - What is a Generative AI Agent?
• 0:23:04 - Agents vs. Workflows: Understanding dynamic flow vs. static paths.
• 0:29:59 - Patterns & Anti-patterns: When not to use an agent.

Part 2: Building the Agent (The "How-To")

• 0:32:36 - Core Components: System prompts, memory, and tools.
• 0:39:00 - Memory Systems: Short-term vs. Long-term implementation.
• 0:43:09 - Python Implementation: From a single LLM call to a full agent.
• 0:54:53 - Frameworks: Building with LangChain.

Part 3: Advanced Architecture

• 1:00:15 - Supervisor vs. Swarm: Comparing architectural patterns.
• 1:04:48 - Swarm Performance: A deep dive into decentralized agents.
• 1:09:05 - Interface Protocols: MCP, A2A, and AGUI.

Part 4: Evaluation & Future

• 1:12:06 - Evaluation: How to benchmark (LLM-as-a-Judge vs. Code-based).
• 1:23:41 - Software 3.0: How development paradigms are shifting.

Discussion: For those of you building locally, are you leaning more toward a Supervisor pattern (central brain) or a Swarm architecture for your multi-agent systems? I'm curious which is proving more stable for production use cases.

https://preview.redd.it/78j9y848t8cg1.png?width=547&format=png&auto=webp&s=b8ab4e93ba38131af2ed6406d4cba3b3e14a540e

https://www.youtube.com/watch?v=tr5Fapv80Cw

Totally worth it, you learn alot about Claude's inner working and what makes it special.

https://anthropic.skilljar.com/claude-code-in-action

I started buiding my own because i was tired using systems that I can't control. I wonder how your experience has been? What your go to?