parithy

Exploring AI & emerging tech 🤖 | Building apps, sharing experiments & practical insights | Software engineer fueled by curiosity

[parithy @parithy]

Google’s Gemini roadmap is shifting from “chatbot” to full agentic systems:
• tools + memory
• proactive workflows
• multimodal reasoning
• long-horizon task execution

The interface is becoming the agent. Apps become capabilities. AI UX is changing fast.

[parithy @parithy]

Anthropic acquiring Stainless is a strong signal: the SDK layer is now core AI infrastructure.

Stainless powered API/SDK automation for OpenAI, Google, and Cloudflare.

The AI stack is evolving from models → apps into full developer ecosystems.

[parithy @parithy]

Agentic AI was about software taking actions.
Physical AI is about software taking responsibility in the real world.

From copilots → coworkers
From workflows → robots
From prompts → perception, planning, and execution

The next interface for AI isn’t just chat.
It’s movement.

[parithy @parithy]

12M token context window 🤯
Subquadratic might’ve just broken the biggest bottleneck in LLMs: quadratic scaling.

If this holds → goodbye RAG hacks, hello full-codebase reasoning in one shot.

[parithy @parithy]

Granite 4.1 shows the playbook for small LLMs:

It’s not about more compute—it’s about better data.

• 5-stage pretraining with progressive data refinement
• LLM-as-Judge for SFT curation
• Multi-stage RL to boost math, coding, and reasoning

Quality > quantity wins again

[parithy @parithy]

The shift:
→ From “code generation” to “code execution”
→ From “prompting” to “environment design”

Sandbox = the new API surface for agents.

Great read 👇
https://t.co/SX52SqyI7I

[parithy @parithy]

Hugging Face last week:

• Open-source is scaling fast — but usage is concentrated in a few breakout models
• RAG is evolving — multimodal embeddings + rerankers > generic vectors

Signal is clear:
distribution is power, and retrieval quality is the new moat.

[parithy @parithy]

Local-first AI is accelerating:

• OpenClaw + llama.cpp → faster batching & longer context
• Pocket LLM v1.5 → on-device voice, OCR, vision (Gemma/FastVLM)
• LlamaIndex ParseBench → standardized evals for parsing

Less cloud. More control. Real performance gains.

[parithy @parithy]

Today’s AI chat UX feels helpful… but it’s often subtly deceptive.
It hides uncertainty, mimics confidence, and nudges users without clarity.

The fix?
👉 Show limits
👉 Expose reasoning
👉 Design for truth, not vibes

AI shouldn’t feel right — it should be right

[parithy @parithy]

From Chain-of-Thought → Compressed Thought → Managed Memory.
LightThinker++ turns reasoning into a memory scheduling problem—fold, expand, commit.
~70% fewer tokens, + accuracy, scalable to long-horizon agents

[parithy @parithy]

IronClaw = what OpenClaw should have been 🔐

Same agent power, but:
• Secrets never touch the LLM
• Tools run in sandboxed WASM
• Security is architecture, not a patch

Shift is clear:
LLM decides → system executes safely

This is the future of agent infra. 🚀

[parithy @parithy]

Claude didn’t win with a radically better model.

The leaks showed the real edge:
•system prompts as control layer
•tool orchestration > raw intelligence
•modular agents + memory loops
•eval-driven behavior

Not magic weights—just elite engineering.

[parithy @parithy]

Google just dropped TurboQuant:
→ 6× less AI memory
→ 8× faster inference
→ 0 quality loss

If this holds, GPUs just got a whole lot “bigger” overnight.

[parithy @parithy]

NVIDIA NemoClaw makes always-on AI assistants actually usable 🔥

An open-source stack that runs OpenClaw inside a locked-down sandbox — every network call, file access, and inference is policy-controlled and routed via NVIDIA Cloud.

Secure by design. Always on. No chaos.

[Prakash Sharma @PrakashS720]

Damnn 😱
Most developers are using Claude Code wrong.

They open the terminal...
write a prompt...
and expect magic.

That’s not where the real power is.

Claude Code is actually a 4-layer AI engineering system:

1️⃣ CLAUDE.md → project memory
Architecture, rules, commands, conventions

2️⃣ Skills → reusable knowledge packs
Testing workflows, code review guides, deploy patterns

3️⃣ Hooks → deterministic guardrails
Security checks, enforced rules, automation

4️⃣ Agents → specialized sub-agents
Break complex tasks into parallel workflows

Once you structure these properly, something interesting happens:

Claude stops behaving like a chatbot.

It starts behaving like a real AI dev system.

Most engineers miss this because they jump straight to prompting.

But the difference between average output and production-level results usually comes down to setup.

If you're building with AI agents in 2026, learn the system — not just the prompt.

I made a Claude Code Starter Pack explaining everything.

If you want it:

Follow
Like + RT
Comment CLAUDE

I'll DM it to a few people.

Future AI dev workflows won't be prompt-first.

They’ll be system-first. 🚀

#AI #Claude #AIAgents #LLM #GenAI

[parithy @parithy]

Claude just made answers visual.

Charts.
Diagrams.
Interactive widgets.
All generated inline in the conversation.
The future of AI replies isn’t paragraphs — it’s mini apps.

https://t.co/i117114uaa

[parithy @parithy]

“Modern local AI stack:
Model → vLLM/llama.cpp → embeddings + vector DB → RAG → OpenWebUI.
Run frontier-level workflows on your own hardware. No API keys required.”

[parithy @parithy]

@BaDahBoomski You can try using from here

https://t.co/rPQNUuV1AX

Good for coding agent

[parithy @parithy]

Nemotron-3 Super is an open 120B hybrid Mamba-Transformer MoE model optimized for long-context reasoning and autonomous AI agents with highly efficient inference.

#nvidia #Nemotron

[parithy @parithy]

Nemotron-3 represents a shift toward agent-first LLMs.

Instead of optimizing just chat benchmarks, it focuses on:
•long-running agents
•tool-use workflows
•multi-step reasoning
•large-context research tasks.