AI Without the Fluff
Reasoning Advances, AWS Agentic AI, and Why Skepticism Still Matter
Today’s selection of AI advancements without hype and fluff. In this post, you’ll find some of the key research updates, AWS announcements, and — as always — the most interesting is at the end, with a bit of skepticism to keep us all sane.
Key Research Advances in AI Reasoning and Fine-Tuning
We’re moving beyond the days of brittle prompt hacks and naive Chain-of-Thought reasoning. A few highlights from recent papers:
Mixture of Reasonings (MoR): This one’s exciting. Instead of hardcoding “reason like this,” models learn multiple reasoning strategies and pick the right one on the fly. Feels more natural and less fragile than the old CoT tricks.
Critical Representation Fine-Tuning (CRFT): A clever hack: tune just a small set of critical neural representations instead of the whole model. Result? Up to 16.4% performance boost on reasoning tasks with barely any compute overhead. Makes fine-tuning both faster and more interpretable.
Vision-Language Models & Multimodal AI: CapRL (Captioning Reinforcement Learning) flips the way we think about “caption quality.” Instead of just prettier descriptions, captions are scored on whether a text-only model can use them to answer questions. A utility-first way of training — and I like that direction.
AWS Innovations in AI Agent Deployment and Model Services
AWS is clearly betting hard on the “agentic AI” future.
Bedrock now includes Qwen 3 and DeepSeek-V3.1 — covering code, reasoning, and multi-tool workflows.
Bedrock AgentCore is the big one: a set of seven core services to build and run secure, scalable AI agents. Think enterprise-ready agent infrastructure instead of DIY prototypes.
Big money flowing: $230M AI accelerator, $100M more for agentic AI, and updates to SageMaker to make optimization less painful.
Automated Reasoning Checks hit up to 99% accuracy — using formal methods to keep LLM outputs honest. Finally, a move away from “trust us, we fixed hallucinations.”
Stability AI diffusion models now in Bedrock: scalable, cost-conscious image generation for enterprises. Feels like generative visuals are maturing into “just another cloud service.”
My take: AWS is quietly building the rails for agentic AI systems the way it did for cloud 15 years ago. Not flashy — but foundational.
Other News
MCP or not to MCP? DeepMind’s Demis Hassabis says Gemini will support Anthropic’s Model Context Protocol. He calls it “a good protocol rapidly becoming an open standard.” Translation: MCP isn’t just hype anymore — it’s sticking.
Something new to try: Perplexity’s new Search API. It gives you access to their web-scale retrieval without building a crawler. I tried it — not magical yet, still nowhere near Google’s indexing — but you can see where it’s heading. Worth experimenting with if you’re building RAG pipelines.
On another level of business:
OpenAI x Oracle x SoftBank: Stargate now ~7 GW across five new datacenter sites. Microsoft bowed out.
CoreWeave x OpenAI: total contracts balloon to $22.4B.
NVIDIA drops $100B into OpenAI. That’s not just partnership — that’s lifeblood.
Mistral raises €1.7B with ASML as lead. Interesting EU semiconductor-AI alignment move.
Thinking Machines Lab lands $2B (!) at a ~$12B valuation.
Modular secures $250M, positioning as a cross-chip infra challenger to NVIDIA’s dominance.
My read: the money keeps getting louder, but the patterns are the same. Consolidation around compute, agent infrastructure, and ecosystem lock-in.
Surprising Findings
Here’s the part that really caught my attention.
Developer productivity paradox: A randomized controlled trial found that experienced open-source devs using early 2025 AI tools (Cursor Pro, Claude 3.5/3.7 Sonnet) actually slowed down. Developers themselves predicted a 24% speedup. Experts forecasted nearly 40%. Reality: slower.
Self-critique loops don’t deliver: A recent arXiv study tested if models improve by critiquing themselves. Results were all over the place. Some gains, but just as often stagnation or decline. Matches the “DeepCritic” findings: LLMs make shallow critics, missing multi-step errors.
So if you’re an experienced engineer feeling skeptical about AI “10x developer” claims — good news: you’re not just grumpy, there’s data backing you up.
Final Thought
AI is moving fast — reasoning frameworks, lean fine-tuning, and enterprise agent infra are real steps forward. But the skepticism isn’t cynicism; it’s survival. Like in ML, overfitting looks like success… until reality shows otherwise.