(alpha) launches, introducing overhauled prompting, much-improved coherence, fine-grained control over small details (hands, text), new upscalers, and a more literal “raw” style option. (reddit.com)

“Gemini” and releases the Gemini Android app, bringing its chatbot to mobile and Search.

(Haiku, Sonnet, Opus), raising the bar on safety and reasoning for large conversational models.

(8 B & 70 B), powering new AI writing assistants in Facebook and WhatsApp.

predicting 3D structures and interactions of proteins, RNA, and DNA at unprecedented scale—transforming molecular biology.

Act, the world’s first comprehensive, risk-based AI regulation (transparency requirements, banned high-risk uses, mandatory safety testing).

(Series D at $2.8 B valuation) to scale its ultra-fast AI inference chips for data
centers.

, offering a 1 M-token context window for advanced document understanding.

a speed-optimized update to Claude 3 with faster inference and improved efficiency.

reissues ERNIE 4.0 Turbo, cutting inference costs by ~90% and announcing 300 M Ernie Bot users

2 (123 B parameters, 128 K-token context) under Apache 2.0—one of the largest permissively-licensed LLMs to date.

launches (7 B parameters specialized for STEM), delivering strong math and logic reasoning for researchers and students.

alongside V6.1, unifying Discord and browser workflows and adding in-browser editing and upscaling. (en.wikipedia.org)

reaches 200 M weekly active users, solidifying AI chat as a mainstream tool.

$1.5 B from Saudi Arabia at LEAP to build an AI data center in Dammam powered by its inference hardware.

, allowing opt-in storage of user preferences and personal details for more natural, personalized output

(1.5 B & 9 B), an open-source coding LLM supporting 52 languages and a 128 K-token context window.

an efficiency-optimized open LLM with top-tier benchmarks at low inference cost.

the fastest member of the Claude family, excelling in high-throughput settings.

its text-to-video model generating 20 s high-def clips (plus a “Turbo” tier for faster output) within ChatGPT.

(developer preview) with native image/audio generation and early agentic tool-use capabilities.

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