Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT - WSJ
Exclusive | The Sudden Fall of OpenAI’s Most Hyped Product Since ChatGPT WSJ
Could not retrieve the full article text.
Read on Google News: ChatGPT →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
productchatgpt
Desktop Nightly v2.2.0-nightly.202604030631
🌙 Nightly Build — v2.2.0-nightly.202604030631 Automated nightly build from main branch. ⚠️ Important Notes This is an automated nightly build and is NOT intended for production use. Nightly builds are generated from the latest main branch and may contain unstable, untested, or incomplete features . No guarantees are made regarding stability, data integrity, or backward compatibility. Bugs, crashes, and breaking changes are expected. Use at your own risk. Do NOT report bugs from nightly builds unless you can reproduce them on the latest beta or stable release. Nightly builds may have different update channels — they will not auto-update to/from stable or beta versions. It is strongly recommended to back up your data before using a nightly build. 📦 Installation Download the appropriate ins
Running Disaggregated LLM Inference on IBM Fusion HCI
Prefill–Decode Separation, KV Cache Affinity, and What the Metrics Show Getting an LLM to respond is straightforward. Getting it to respond consistently at scale, with observable performance, that’s where most deployments run into trouble. Traditional LLM deployments often struggle with scaling inefficiencies, high latency, and limited visibility into where time is spent during inference. Red Hat OpenShift AI 3.0 introduces a new inference architecture built around llm-d (LLM Disaggregated Inference), which separates the Prefill and Decode phases of LLM inference into independently scalable pod pools. This approach addresses key challenges by isolating compute-heavy and memory-bound workloads, improving KV cache reuse across requests, and enabling fine-grained observability into each stage
Why LLM Inference Slows Down with Longer Contexts
A systems-level view of how long contexts shift LLM inference from compute-bound to memory-bound You send a prompt to an LLM, and at first everything feels fast. Short prompts return almost instantly, and even moderately long inputs do not seem to cause any noticeable delay. The system appears stable, predictable, almost indifferent to the amount of text you provide. But this does not scale the way you might expect. As the prompt grows longer, latency does increase. But more importantly, the system itself starts behaving differently. What makes this interesting is that nothing external has changed. The model and hardware is same. But the workload is not. As sequence length grows, the way computation is structured changes. The amount of data the model needs to access changes. And the balanc
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Models
Why LLM Inference Slows Down with Longer Contexts
A systems-level view of how long contexts shift LLM inference from compute-bound to memory-bound You send a prompt to an LLM, and at first everything feels fast. Short prompts return almost instantly, and even moderately long inputs do not seem to cause any noticeable delay. The system appears stable, predictable, almost indifferent to the amount of text you provide. But this does not scale the way you might expect. As the prompt grows longer, latency does increase. But more importantly, the system itself starts behaving differently. What makes this interesting is that nothing external has changed. The model and hardware is same. But the workload is not. As sequence length grows, the way computation is structured changes. The amount of data the model needs to access changes. And the balanc
What If You Could Break Your API Design Before Writing a Single Line of Code?
I don’t write code. I’ve never written code. I direct AI coding agents — Claude Code, mostly — and they build what I describe. Over the last few months, I’ve been building a series of single-task AI agents, each one proving a different idea about how autonomous software should work. Agent 004 was a red team simulator. It attacked my own infrastructure from the outside — over HTTP, with its own identity, posting real collateral before every action. It ran 15 predefined attacks, then learned to adapt its strategy across rounds, then started writing its own novel attack code and executing it in a sandboxed child process. By the time it was done, it had thrown more than a hundred adversarial scenarios at the system and, in the tested runs, surfaced no exploitable paths. The sandbox it used — f
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!