DeepSeek V4 packs top-tier specs rivaling elite open models. Its 1M-token context window handles vast inputs effortlessly. V4 Pro dominates Vals AI’s Vibe Code Bench and scores near GPT-5.4 on reasoning evals. Huawei Ascend chip support bypasses Nvidia reliance for efficient runs. Pricing at $1.74/1M input and $3.48 output undercuts GPT-5.5’s $5/$30 sharply. V4 excels […]
Meta is slashing 10% of its workforce—around 8,000 jobs—while leaving 6,000 positions unfilled, shrinking headcount by 14%. Microsoft targets 8,750 U.S. employees with voluntary buyouts, hitting 7% of its 125,000-strong staff. These cuts hit right as both pour billions into AI data centers and elite talent, prioritizing infrastructure over broad payrolls. This isn’t isolated. Amazon
Big Tech companies spend massive sums on federal lobbying, dropping $20 million in Q1 2026 alone—about $226,000 daily across 11 major players like Meta, Alphabet, and AI firms Anthropic and OpenAI. Meta led with $7.1 million, while AI outfits hit records amid regulatory fights. This surge builds on 2025’s $109 million peak, fueling armies of
OpenAI has struck back in the AI race, launching GPT-5.5 ‘Spud’—a powerhouse model topping benchmarks in reasoning, coding, agentic tasks, and computer use. It outpaces rivals like Anthropic’s Claude, matching even elite scores while keeping the speed of GPT-5.4 and slashing costs to $5/$30 per million tokens. Self-optimizing its own GPU code, ‘Spud’ signals a
Anthropic’s security crisis ignited in late March 2026 with a misconfigured system leaking nearly 3,000 internal files. This breach exposed early details on Mythos (aka Capybara), their ultra-powerful cybersecurity AI deemed too risky for public release. Days later, a second fiasco dumped over 500,000 lines of Claude Code source across 1,900 files. It handed sleuths
ANTHROPIC’S MYTHOS LEAK: CODE SLIPS TO DISCORD BREACH Read More »
OpenAI’s new ChatGPT Images 2.0 has dethroned Google’s Nano Banana, which ruled leaderboards for nearly a year. After early hits like DALL-E and GPT Image 1, this upgrade tops every category on Arena AI’s text-to-image charts. It generates stunning 2K images up to eight at a time, in any shape from ultrawide to tall, and
No single AI wins every race. A coding champ like Claude 3.5 Sonnet shines in debugging police case scripts or refactoring podcast automation code, but it lags in creative storytelling for your next episode hook. Teams win by matching models to missions — outcomes over hype. Pick by task: For general chat and productivity, lean
STOP ASKING ‘BEST AI MODEL.’ ASK ‘BEST FOR WHAT?’ Read More »
Honor’s bright-red Lightning bot stunned crowds at Beijing’s half-marathon on Sunday, crossing the 21km finish line in 50:26 — nearly seven minutes faster than the human world record set by Jacob Kiplimo. Over 100 Chinese-made humanoids raced in a dedicated lane alongside 12,000 human runners, marking just the second year robots joined the event. This
HUMANOID REVOLUTION ACCELERATES: ROBOTS SMASH HALF-MARATHON RECORDS Read More »
The breakthrough behind Canva’s new “Design AI Model” lies in training on the sequence of actions, not just finished designs. Instead of learning only from static images, the model ingests millions of real designs along with every click, layout shift, and style change made by users, capturing how people actually arrive at good work over
DESIGN AI THAT LEARNS HOW HUMANS ACTUALLY DESIGN Read More »
Anthropic just launched Claude Opus 4.7, their newest AI brain that’s better than rivals like GPT-5.4 and Gemini 3.1 at tough coding tasks. It scores 64.3% on a key test called SWE-bench Pro—up from the old version’s 53.4%. But their super-secret model, Mythos Preview, still crushes it at 77.8%. Pricing stays the same, though it
ANTHROPIC UNVEILS CLAUDE OPUS 4.7: TOPPING RIVALS BUT CHASING ITS OWN SECRET WEAPON Read More »
OpenAI has transformed Codex from a mere coding agent into a powerhouse superapp, integrating ChatGPT, Atlas, and advanced tools. The latest update introduces background computer use, allowing Codex to independently operate Mac apps — even those without APIs—while running multiple parallel agents simultaneously. This marks a shift toward seamless, autonomous AI assistance beyond traditional coding.
