Groq announces a landmark deal at LEAP 2025 to build a Groq-powered AI data center in Dammam. Their deterministic inference chips promise 2× lower latency than GPUs, targeting hyperscale LLM deployments.

• Size & Context: ~200 B parameters, 128 K-token window (4× GPT-4’s)

• Capabilities: Dramatically improved long-form reasoning—summarizes entire books in one go—and native code execution sandbox for safe plugin trials.

• Benchmarks: +15 pts on MMLU vs. GPT-4, near-human scores on law/professional exams.

A new research paper introducing an evolutionary-search layer for LLM fine-tuning. By simulating “mutations” in model weights and selecting top performers, AlphaEvolve achieves 20 % faster convergence on reasoning tasks while requiring 30 % less compute.

(developer preview)
• Deep Think Mode: Adaptive multi-step reasoning pipelines that decompose complex queries into tool-calls + internal chains.
• Multimodal Upgrade: First model to natively process 4K-resolution images and 60 s audio clips in a single pass.
• API Beta: Available to 500+ enterprise partners for feedback ahead of public launch.

In their Discord Office Hours, Midjourney teases V7’s “Neural Style Transfer 3.0,” promising photo-realism indistinguishable from professional photography, plus “Conceptual Blending” for merging vastly different motifs.

• Architecture: 600 B parameters with a dual-stream vision-language core.
• Performance: Outperforms GPT-4o on Chinese reasoning benchmarks by 10 %; 50 % faster inference via sparsely activated experts.

at Baidu Create 2025
• ERNIE 4.5 Turbo: 180 B-param multimodal LLM with 256 K-token context.
• X1 Turbo: Lightweight 50 B model optimized for real-time voice assistants in UX devices.
• Ecosystem: Rolled into Baidu Maps, Apollo Auto, and iQiyi video recommendations.

Sonnet-4 (fast, 75 B), Opus-4 (balanced, 200 B)
• New Feats: Real-time code execution with rollback safety; mixed-modal “WhisperSpeak” audio dialogue; 64 K-token memory that persists across sessions.
• Safety: New guided-alignment layer reduces hallucinations by 35 % in internal stress tests.

: “Deep Think” for complex reasoning; “Flash” for sub-50 ms replies on common queries.
• Agentic Tools: First wide release of autonomous agents that can browse, book appointments, and handle multi-step transactions.
• SDK: Now supports JavaScript, Python, and a low-code “PromptFlow” drag-drop interface.

• Magistral Small (24 B, Apache 2.0): Reasoning-focused, excels on logic/math benchmarks (GSM8K, MATH)
• Magistral Medium (proprietary 60 B): Enterprise-grade with built-in retrieval augmentation and vector DB plugins.

Why this works:

- The model better understands what you're asking.
- It rephrases the task in its own words, making things clearer.
- It becomes more flexible and useful across different tasks.

How to do it:

• Give the model a basic denoscription of your task.
- Ask: “What’s the best way to phrase this as a prompt?”
- Refine its suggestion with feedback and examples.
- Let the model suggest improvements that work more generally.
- Ask it questions to make sure it really understands the task.

Why this works:

• Pushes the model to give better answers—not just "okay" ones.
• Mimics how people review and improve their own work.
• Helps avoid lazy or half-baked responses.

How to do it:

- After the model gives a response, ask it to rate its answer from 1 to 10.
- If it rates below a 9, ask it to improve the answer.
- You can also ask it to double-check if it followed your rules.

Why this works:

- Gives you more consistent results—especially for technical or repetitive tasks.
- Examples reduce confusion without making your prompt too long.

How to do it:

• Give the model a clear format to follow (like JSON or a text template).
• Share 1–2 good examples of what you’re looking for.
• Briefly explain why those examples work well.
• Only show bad examples if it’s really necessasy.

Why this works:

- It’s easier for the model to handle simple steps one at a time.
- Each step is done better when it's not overloaded.

How to do it:

• Divide the task into smaller     parts (e.g., write → review → revise).
• Use separate prompts for each part.
• If you’re working on something big, tools like LangChain can help manage the flow.

Why this works:

- You can spot where the model went wrong or misunderstood something.
- It helps you fix prompts faster and more precisely.
- Makes debugging and improving your workflows easier.

How to do it:❓

After getting a result, ask: “Why did you respond like this?”
Use the explanation to tweak your prompt or process.

✅ 300 free credits daily
✅ 1,000 bonus credits at sign-up
✅ Gaia Score: 70%+ (GPT-4 gets only 15%)
✅ Used by Jack Dorsey, MIT & Bloomberg-tested