Founder, Trend Wire Group
Yash Malviya is the founder of Trend Wire Group and editor of AI Newspedia. Yash chooses the AI and technology stories worth scrutinising, gathers the primary sources behind them, and uses AI tools to structure and sharpen the reporting; every article is then cross-checked against its sources, edited and finalised by hand before publication.
Background: Editorial team member at AI Newspedia; researches AI tools, model releases and industry news, verifies each claim against its primary source, and edits AI-assisted drafts to publication.
A vector database stores embeddings, the numerical fingerprints AI models make for text and images, and finds the ones closest in meaning to a query. That nearest-neighbour search is what powers semantic search and RAG. Here is what it does, and when a library or a Postgres extension will do the same job.
Anthropic released Claude Sonnet 5.5 on 28 September 2026. It runs more than 30% faster than Sonnet 5 and costs up to 30% less for most work, at the same per-token price. On Anthropic's own benchmarks it beats the flagship Opus 5.5 on one agentic test, though independent evaluations are not in yet.
Fine-tuning is one of the most misunderstood levers in AI. It changes how a model behaves, its format, tone and consistency, not what it knows. Here is what fine-tuning actually does, how the cheap version called LoRA works, and the rule for when to fine-tune instead of reaching for retrieval or a longer prompt.
Everyone has an opinion on prompt engineering, few can define it. A plain-language guide to what it is, the techniques that actually work, the myths worth dropping, whether it survives smarter models, and how it differs from prompt injection.
AI tools bill by the token, not by the task, which is why the same job can cost pennies in one app and real money in another. Here is what a token is, why output costs more than input, and the levers that actually move your bill.
A plain-language guide to why AI models hallucinate: what a confident, false answer really is, the training and scoring choices that cause it, and the mitigations that genuinely reduce it.
A US appeals court voted 2-1 to uphold the Pentagon's designation of Anthropic as a national-security supply-chain risk, after the company refused to let Claude be used for autonomous weapons or mass surveillance. It is a rare, concrete test of what an AI lab's safety red lines cost when the customer is the military.
At a Reuters event on 25 September, FTC Chair Andrew Ferguson rejected the idea that AI agents are autonomous actors that 'break loose' on their own. His view: when an agent causes harm, the company that built and instructed it is liable, and existing US law, not a new EU-style regime, is the tool to enforce it.
Mark Zuckerberg told NBC News that AI needs no industrywide slowdown, with each lab policing itself instead. He said it days after Dario Amodei and Sam Altman urged the UN Security Council to coordinate one. The frontier labs are now openly split on whether AI safety can be left to the market.
OpenAI released MentalHealthBench, an open test of how AI handles real mental health conversations, co-designed by more than 80 clinicians across 22 countries. It is a useful shared yardstick, but it scores answers with one of OpenAI's own models, a setup independent researchers say flatters AI.
Microsoft merged its consumer and workplace Copilot into one app with three tabs: Home, Code and Autopilot. It pitched the result as 'the AI built for work.' Read the fine print and it is also Microsoft conceding the personal chatbot race to ChatGPT and Gemini, to defend the ground it holds: the enterprise.
The Office of the National Cyber Director asked OpenAI and Anthropic to run new models past US testers before handing them to the UK's AI Security Institute, once the world's most privileged early tester. Anthropic already complied with Mythos 5.1. It is the quiet nationalization of AI safety testing.