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Global AI market size — Grand View Research
Have adopted AI in at least one function — McKinsey
Annual value AI could add — McKinsey Global Institute
One real distribution or SEO trick per day. Stolen from products that grew without a marketing team.
Kimi K3 didn't try to win on being the cheapest option or claim to beat the frontier labs outright. It priced near the market rate and let its own benchmark sheet openly admit where it falls short -- and that honesty is what made the comparison to the frontier labs credible in the first place.
Thinking Machines made Inkling's weights free and available on every major inference host on day one, then put its actual monetization -- the Tinker fine-tuning platform -- behind a limited-time discount instead of a hard paywall. Giving away the part with no defensibility funds demand for the part that does.
The disclosure didn't just describe a vulnerability. It published a dated timeline of every attempt to reach the vendor, which turned seven months of silence into the most persuasive part of the story.
Every day, one trending AI idea turned into a step-by-step money-making guide.
Kimi K3 publishes a clean rate card -- "$0.30" per million cache-hit input tokens, "$3.00" cache-miss, "$15.00" output -- and strong scores at "max reasoning effort" (GPQA-Diamond "93.5", Terminal Bench 2.1 "88.3"). But a 2.8-trillion-parameter model that activates 16 of 896 experts and runs in a reasoning mode can burn wildly different token counts per task depending on the prompt. The headline $/M rate tells a buyer almost nothing about what a real workload will actually cost until someone runs their own tasks through it and counts the tokens.
Thinking Machines released Inkling, a mixture-of-experts model with 975B total parameters and 41B active parameters, trained on 45 trillion tokens, with full weights on Hugging Face, a free Playground for a limited time, and '50% discount for a limited time' on its Tinker fine-tuning platform. On Hacker News, one comment called the setup a 'great business model' where companies own personalized models while Thinking Machines provides the infrastructure -- but a skeptic countered there is 'no moat' in LLM services since switching between open-weight models means just changing an API endpoint. That tension is the service opportunity: most teams don't have the bandwidth to evaluate whether a new open-weight model is actually good enough to switch to, or the skill to fine-tune it for their own workflow once they decide to.
A researcher found that Cursor on Windows "automatically executes a malicious git.exe binary placed in a repository root with no user interaction, warnings, or prompts," resulting in arbitrary code execution. The bug was reported on December 15, 2025, and the researcher says it persisted through "197+ new versions" before the researcher published full disclosure on July 14, 2026. Cursor is used by "7 million+ active users, 1 million+ daily, 1 million+ paying, used by 50K+ companies," at a reported valuation of "$60 billion" — which means an unpatched, silent, arbitrary-code-execution bug sat inside one of the most widely deployed AI coding tools for seven months with no user-facing warning.
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