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Enterprises building voice-enabled workflows have had limited options for production-grade transcription: closed APIs with data residency risks, or open models that trade accuracy for deployability. Cohere’s new open-weight ASR model, Transcribe, is built to compete on all four key differentiators — contextual accuracy, latency, control and cost. Cohere says that Transcribe outperforms current leaders on …

Cohere’s open-weight ASR model hits 5.4% word error rate — low enough to replace speech APIs in production pipelines Read More »

Last week, one of our product managers (PMs) built and shipped a feature. Not spec’d it. Not filed a ticket for it. Built it, tested it, and shipped it to production. In a day. A few days earlier, our designer noticed that the visual appearance of our IDE plugins had drifted from the design system. …

When product managers ship code: AI just broke the software org chart Read More »

Many people have tried AI tools and walked away unimpressed. I get it — many demos promise magic, but in practice, the results can feel underwhelming. That’s why I want to write this not as a futurist prediction, but from lived experience. Over the past six months, I turned my engineering organization AI-first. I’ve shared …

When AI turns software development inside-out: 170% throughput at 80% headcount Read More »

Processing 200,000 tokens through a large language model is expensive and slow: the longer the context, the faster the costs spiral. Researchers at Tsinghua University and Z.ai have built a technique called IndexCache that cuts up to 75% of the redundant computation in sparse attention models, delivering up to 1.82x faster time-to-first-token and 1.48x faster …

IndexCache, a new sparse attention optimizer, delivers 1.82x faster inference on long-context AI models Read More »

Presented by OutSystems After two years of flashy AI demos, rushed agent prototypes, and breathless predictions, enterprise technology leaders are striking a more pragmatic tone in 2026. In a recent webinar hosted by OutSystems, a panel of software executives and enterprise practitioners made the case that the most consequential AI work happening now is focused …

The consequential AI work that actually moves the needle for enterprises Read More »

Intercom is taking an unusual gamble for a legacy software company: building its own AI model. The 15-year-old, Dublin, Ireland-based massive customer service platform announced Fin Apex 1.0 on Thursday, a small, purpose-built AI model that the company claims outperforms leading frontier models from OpenAI and Anthropic on the metrics that matter most for customer …

Intercom’s new post-trained Fin Apex 1.0 beats GPT-5.4 and Claude Sonnet 4.6 at customer service resolutions Read More »

The enterprise voice AI market is in the middle of a land grab. ElevenLabs and IBM announced a collaboration just this week to bring premium voice capabilities into IBM’s watsonx Orchestrate platform. Google Cloud has been expanding its Chirp 3 HD voices. OpenAI continues to iterate on its own speech synthesis. And the market underpinning …

Mistral AI just released a text-to-speech model it says beats ElevenLabs — and it’s giving away the weights for free Read More »

Enterprise data teams moving agentic AI into production are hitting a consistent failure point at the data tier. Agents built across a vector store, a relational database, a graph store and a lakehouse require sync pipelines to keep context current. Under production load, that context goes stale.  Oracle, whose database infrastructure runs the transaction systems …

Oracle converges the AI data stack to give enterprise agents a single version of truth Read More »

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the “Key-Value (KV) cache bottleneck.” Every word a model processes must be stored as a high-dimensional vector in high-speed memory. For long-form tasks, this “digital cheat sheet” swells rapidly, devouring the …

Google’s new TurboQuant algorithm speeds up AI memory 8x, cutting costs by 50% or more Read More »

Standard RAG pipelines break when enterprises try to use them for long-term, multi-session LLM agent deployments. This is a critical limitation as demand for persistent AI assistants grows. xMemory, a new technique developed by researchers at King’s College London and The Alan Turing Institute, solves this by organizing conversations into a searchable hierarchy of semantic …

How xMemory cuts token costs and context bloat in AI agents Read More »