Metaasked rivals likeMicrosoftfor cash to handle its soaring AI expenses. Bold move, right? Say hello toLlama 4—a beast with next-gen scalability. Think 10 million token contexts and a slickMixture-of-Expertsdesign. Legal drama over training data could crank up costs, butMetaplays it smart, pushing ..

AutoRAG in open beta simplifies how developers integrate context-aware AI into their applications by setting up a fully managed Retrieval-Augmented Generation pipeline on Cloudflare. With AutoRAG, developers can efficiently fetch information from their own data sources to improve AI responses using ..

Picking the right AI model forGitHub Copilotis like matchmaking. It's about the project's quirks, and balancing razor-sharp accuracy with processing muscle...

Gemini 2.5 Flashbursts into the scene with a sparkling new feature: a "thinking budget." This lets developers fine-tune token-based reasoning anywhere from 0 to a whopping 24,576, cranking up accuracy without gouging your pockets. Catch it in preview onGoogle AI StudioandVertex AI. The model handles..

Creating a degree19odd-power polynomial with a linear coefficient of-19is not your usual algebra homework. Get cozy withT19(x), because factorization demands finesse here. Aim to break it down into at least three stubbornly irreducible pieces. The trick? Jugglingnon-linear factorsto dodge any slip i..

Gemini 2.5 Flashis your quick-thinking friend with an on/off brainstorm switch, juggling the holy trinity: quality, cost, and speed. It tacklesHard Promptslike a pro, only overshadowed by 2.5 Pro...

Microsoftplays it cool with an "off-frontier" AI strategy, sidestepping heavyweights likeOpenAI. It's a cost-cutting, reliability-boosting move. Even with deep pockets sunk intoOpenAI,they're building pint-sized brainiacs with theirPhi project. The grand plan? Stand-alone strength by 2030...

OpenAI's new GPT-4.1 family enhances coding abilities at a lower cost, outperforming predecessors and offering up to one million tokens of context processing. The move challenges competitors with more affordable models tailored to diverse enterprise needs...

3FSfrom DeepSeek dazzles with slick tricks, includingCRAQfor ironclad consistency and a cleverChunkEnginebuilt inRust. It sprints through scalable reads, but gets tripped up bywrite latency. InZipfian workloads, that bottleneck might just drive you bananas...

GPT-4o, Llama-4, Claude 3.7 Sonnet. Why can’t AI companies come up with compelling model names?..