Great article. This is exactly what we're doing from a product perspective.
What if the frontier-minus-6-months assumption does not hold? The US has 5x the AI Capex of China, and 10x the EU. Assuming AI is compute limited (we certainly seem to be given the RAM crisis) - wouldn't it be reasonable to assume frontier models are likely to continue to pull ahead?
The harness is really important. It matters so much - possibly even more than the model. We had harness crashes after running many agents - granted we were doing quite a bit with it. Grok Build (as a product) review here:
UX friction behind is worse than Claude Code - but seems to be a strange positioning choice - they're more on the 'vibe' side than the 'agentic engineering' things.
Largest issue was actually reviewing output - but if you're going to largely make that opaque from the user, why choose a CLI-based interface that's so mouse-heavy?
There's also problems with the actual model. Thinking is visible, and every interaction goes like this:
"I would like you to investigate adding an API route to tackle x,y,z"
*Grok, thinking: Okay - the user has asked me to add an API route to tackle x,y,z"
Also absolutely absurd other quirks - "I have no tools available in my context" being visible in the CoT.
The auto-approval (yellow, auto-mode) review of Claude Code via Opus is a killer feature - every build-it CLI should be offering this for long horizon tasks.
Messaged one of the engineers about our experience - no feedback.
You'd be better off with Claude Code 5x Max than the 300 USD/month subscription.
The cost of compute and inference efficiency keeps dropping. Deepseek has numerous advantages here that Anthropic has not likely implemented.
If we assume the current rate of 10x reduction every year or so, profitability is inevitable. It’s just a market-share cash-fight at the top right now.
It's very hard to compete with the massively-token-subsidized big players when our entire team is spending nearly 20x the claude code subscription cost in API token usage - it's impossible for anyone else to do it without eating huge losses.
Junie - their coding agent - was also a miss. I've had Rider for almost 10 years currently - but considering dropping it.
Tradcoding is basically dead, and a lightweight text editor with tree-sitter has come a long way - and it's good enough to read/micro edit with anyway.
I feel bad for them as it's been such a stable product for decades of excellent development, but the world moves on.
Maybe that's not how it's being used though - Nobody needs photoshop to solve a specific focused problem.
Photoshop is a (formerly?) great toolbox. Toolboxes are good if you need to cater to a wide audience. An audience of one via bespoke software - the real revolution - doesn't need the full photoshop experience.
Countless examples previously requiring photoshop are now replaced with some ffmpeg and imagemagick pipelines written by AI daily.
There's so much tracking on this site it's even running WebGL to try and fingerprint the browser. Is that really necessary for a joke site, or does this ship by default with every CloudFlare site?
LLMs are rapidly becoming the first 'purely digital commodity'.
Being digital it's somewhat hard to apply any kind of trade protectionism or Chicken Tax onto them. Maybe there's a market for cruelty-free vegan non-GMO (low-water-use sustainable energy) LLM tokens as well as European ones?
I really like what Mistral did for open Models - but what is the plan to compete against the likes of Moonshot, DeepSeek in the global market? When you can get Kimi K2.6 served via cloudflare it raises tough questions on the economics of it all.
What exactly is Mistral's strategy is aside from niche regulatory requirements or a Eurocentric hedge for AI sovereignty? Do they even have ambitions to compete on the global stage?
This is such a sorely needed point of integration. Cool to see Peter still shipping tools. It’s such a pity meta refuses to play ball like Telegram.
Either they’ll double-down and make this even harder -or- hopefully realise that WhatsApp is likely to be a really common control plane for AI systems in the next few years. Let’s hope the Llama energy strikes and it’s the latter.
From our look into it - amazing speed, but challenges remain around time-to-first-token user experience and overall answer quality.
Can absolutely see this working if we can get the speed and accuracy up to that “good enough” position for cheaper models - or non-user facing async work.
One other question I’ve had is wondering if it’s possible to actually set a huge amount of text to diffuse as the output - using a larger body to mechanically force greater levels of reasoning. I’m sure there’s some incredibly interesting research taking place in the big labs on this.
The market timing on this is perfect - it fills a major current gap I've seen emerging.
I've heard a few stories of QA departments being near-burnout due to the increased rate developers are shipping at these days. Even we're looking for any available QA resources we can pull in here.
No harm meant with the question - but what's the advantage over Claude Code + the GitHub integrations?
https://mindfront.ai