Three solid options, zero clear winner. The confusing part? The newest M4 (32GB) is supposedly the slowest for inference speed, while the M2 Pro (32GB) actually pushes more tokens per second. Then there's the M1 Max (64GB) with that fat memory bandwidth that older chips are weirdly good at. If you're running Ollama daily with coding assistants like Qwen or Kimi, plus maybe some RAG pipelines, the math gets messy fast. Budget's $2–3k so you're not totally broke on options — but which one actually delivers? Real-world experience beats spec sheets every time.
OpenAI is hosting a livestream event. Details about the specific announcements, product launches, or demonstrations will be revealed during the broadcast.
The last time OpenAI did an unannounced livestream, they dropped GPT-4 Turbo and changed pricing overnight
OpenAI is launching ChatGPT Images 2.0 with major upgrades to image generation capabilities. Watch the livestream announcement at https://openai.com/live/
OpenAI is positioning this as a direct competitor to established image generation tools, suggesting they're confident enough to challenge the current market leaders
Throughout 2025 the standard response to any complaint about an LLM was some version of "just wait 3-6 months, the next generation will handle this effortlessly." The argument was everywhere. Every limitation was temporary, every missing capability was a few iterations away, every autonomous agent demo was a preview of imminent reality. It's April 2026 now and worth checking how that held up. On r/ClaudeAI this week there's a long thread about Opus 4.7 where multiple users argue it's a regress
Someone actually tested Mistral Medium 3.5 on AMD's new Strix Halo chip, and the results are... not great. For a 48k-token prompt with 4k thinking tokens, it took about 2 hours just to get an answer about code architecture. Yeah, you read that right—two hours. The takeaway: if you want to run this locally on Strix Halo, queue it up before bed. The technical setup involved heavy optimization (Q5_K_XL quantization, GPU acceleration with -ngl 999, cache reuse), but even with all that tuning, it's still a crawl. Not exactly the "instant local AI" dream, but hey, at least it works.
Tech Blogger Take
Apple's chip specs are lying to you — the 'slower' M2 Pro is crushing the M4 for AI work
Here's something that'll make you question everything: the shiny new M4 Mac Mini is getting smoked by the older M2 Pro when it comes to running local LLMs. We're talking real tokens-per-second measurements, not Apple's marketing fluff. The M1 Max with 64GB is sitting there like the dark horse with its massive memory bandwidth, while everyone's obsessing over the latest silicon. If you're running Ollama daily with coding assistants or building RAG pipelines, this performance gap isn't academic — it's the difference between smooth workflow and waiting around for responses. The weirdest part? The older chips seem to have some secret sauce that newer architectures lost. Your $2-3k budget suddenly got a lot more complicated because the newest isn't the fastest.
AI Analysis
Software Development
highTest your actual workload on each chip before buying — inference speed varies wildly by model size and your specific use case
The M2 Pro is outperforming the newer M4 in real-world token generation, which completely flips the 'newer is better' assumption
Your daily coding workflow with AI assistants could be 30% faster or slower depending on which Mac you choose, and the specs won't tell you which
Job Impact Analysis
AI Engineer
Role ShiftLocal LLM performance directly affects iteration speed when building RAG pipelines and testing model responses
Benchmark your specific models on each chip configuration before committing — don't trust marketing specs
Software Developer
OpportunityRunning coding assistants locally means no API costs and faster response times for daily development work
Calculate your current AI API spending — local inference might pay for the hardware upgrade in 6 months