I downgraded my Claude subscription from $200/mo to $100/mo and started paying Cursor $60/mo instead. The trigger was Wednesday's Claude outage, but the real reason is that GLM-5.2 through Cursor has been fast enough and good enough that the math stopped favoring Opus. If a model is 80% as good as Opus 5 but roughly 7x faster, I'll take the speed and pay for the 80%.
The unscientific test
Same workspace, same CLAUDE.md/AGENTS.md, high effort on both. The prompt was "make a quick tetris game as an html file," followed immediately by "now use cyberpunk colors."
Two setups: Cursor running GLM-5.2, and Claude Code running Opus 5.
GLM finished in 53 seconds. Opus took 7 minutes 5 seconds, though a good 2-3 minutes of that was Opus testing the game with Playwright, which GLM didn't do.
Opus-5 - Tetris game interface showing score 148 on level 1 with various colored falling blocks on a grid and control instructionsGLM-5.2 - Retro Tetris game screen with neon purple border, colorful falling blocks in play area, score 144, level 1, and control instructions displayed on right panel
The visuals came out mostly the same. Opus edged it on the blank-slate UI, which tracks with my experience. I trust Opus more when there's nothing on screen to anchor a design. The generated code was close to identical in structure.
Adding Kimi to the pile
For a third data point, Cursor with Kimi K3 on max effort: 4 minutes 3 seconds.
Kimi-3 Tetris game interface showing cyan I-piece at top, stacked colorful blocks below, with controls on left, next pieces and score (211) on right
Visuals are fine. The code has a bug, the game doesn't auto-start, and the structure feels a step down: a big list of global variables where Opus and GLM both scoped things more tightly. Again, totally unscientific, but Kimi felt buggier and the code quality "felt" lower.
The 7x
Experiencing near-Opus quality at 7x the speed is genuinely a different thing than reading about it. The loop tightens. You ask, you get an answer, you ask again, all before you've lost the thread.
If this is what 7x feels like, I'd like to see 70x.
AI can grind out game content that used to eat weeks of human labor, but it still needs a human with taste sitting between the output and the ship. That's the real lesson from our latest SpaceMolt post, where adding a single dish, pizza, turned into an entire industrial civilization. The first slice in an empty galaxy costs about 28.5 million credits to bring into existence. The second one costs 127.
Futuristic mining facility with multiple illuminated conveyor belts carrying ore, topped by a tall tower with glowing sections against a starry purple sky
The pizza is a supply chain
Nobody hands your agent a pizza. To serve one slice you have to grow the crust, hunt three species for the sauce, tame a herd of belt-grazers for the cheese, and mine your way down to bare ore for all of it. Craig traced the whole thing against live game data: 101 distinct item types, 29 facilities to build, 9 levels deep from ore to slice. The cheese alone means founding an outpost, leveling a science skill, feeding livestock on an iron-ore supply line, and defending the herd with diplomacy because there are no cops in lawless space.
That depth didn't fall out of a prompt. Craig built the food and crafting system with Claude, and the volume of names, flavor text, and interlocking recipes is exactly the kind of grunt work an agent is good at. But I'd bet money he didn't let Claude invent a hundred item names and just ship them.
The judgment is the job
I didn't work on this feature. I'm doing more of the executive and growth side of the game, so I watched this one from the outside, which is maybe why the pattern is easier to see.
The face-value read is that AI automated the content grind. The truer read is that a person still had to munge all of it. Something has to decide that "a still-smouldering organ from a cinder-sylph" is the right line and the twelve other candidates aren't. Something has to notice that the ranch, the milk, and the cull target fold into each other in a way that feels earned instead of arbitrary. That something is a human with sensibilities.
The same thing is happening in software engineering, and most of us are learning it in real time. Agents take away the grunt work. They do not take away the review. You still have to look at what came out and use your own judgment about what wins, even if you only read it at a high level.
If you like MMOs, or systems that turtle all the way down to bare rock, the full post walks the whole chain backward from the customer to the ore.
I've been a heavy Claude Code user since I dropped off Cursor back in January, and after half a year of running Opus on high effort as my daily driver, I finally sat down to see what the other two frontier options actually feel like: GPT-5.6 Sol inside Codex, and GLM-5.2 inside Cursor. This is a first pass, not a benchmark. But the differences show up fast, and they're less about raw capability than about how much steering each one needs.
Why I stuck with Claude Code
Claude Code with Opus has been reliable in a specific way: I know where the edges are. Tell it to do something and I generally know the outcome before it runs. That predictability is worth a lot, even through Anthropic's downtime and the occasional sense that a model's behavior shifted underneath me. Reliability beats novelty when the tool is load-bearing.
Curiosity won anyway. Both Sol and GLM-5.2 have a lot of hype around them, so both are getting a real trial on real work.
GLM-5.2 in Cursor
The initial, unscientific impression of GLM-5.2 is that it's good. Call it 75 to 80% as capable as Opus 4.8. It gets things done without much intervention and mostly stays on the rails, though it sometimes wanders down an implementation path that needs correcting.
Two confounders help it, and I want to be honest about them. One, Cursor is a good harness. Two, our team runs an extensive developer harness module: skills, documentation, helper scripts, a large pile of team-specific context that gives any model an edge. How GLM-5.2 would perform naked, hitting the API through a simpler agent, I genuinely don't know.
One habit across all of this: force thinking as high as the tool allows. Not max, usually the tier just under it, like high.
GPT-5.6 Sol in Codex
Sol on high thinking seems reasonable on capability. The problem so far is inertia.
Ask it to do something, or suggest something be done, and it says sure, it can do that. Say "okay, go do it," and it says yes, it can absolutely do that. Then I have to say, explicitly, no, go do the thing, now. It reads the letter of what you wrote and pays less attention to the intent behind it, so you have to give it more direct instructions than I'm used to giving.
That friction is hindering my evaluation more than any capability ceiling is. I'll keep pushing on it, because the pedantry might just be a prompting problem on my end.
Where I've landed for now
Big tasks and anything that needs writing, design docs, blog posts, stay in Claude with Opus. Small and medium tasks get delegated out to Cursor and GLM-5.2. Sol stays in the rotation as an experiment until I figure out whether the inertia is the model or me.
Betterpost is live, and it's now an MCP server you add to Claude or GPT. Tell it the topic you want to follow, and it scours hundreds of articles, ranks them for relevance, and writes you a newsletter or blog post that reads like a person wrote it. It's free to start with 100 credits.
BetterPost website homepage displaying AI writing tool features, example newsletter about green energy, and chatbot integration options
The thing I actually built
About a year ago I wanted to stay current on a few narrow topics: AI coding, type 1 diabetes science, and celiac science. The last two were for family members. The reading was more than I could keep up with, so I built a system to collect sources, find the articles worth reading inside them, and assemble email newsletters I'd actually want to open.
That system became Betterpost. It's been powering my own mailing lists for a year now, over 1,200 subscribers and open rates near 40%. The newsletters aren't slop. They're the ones I read first.
Why this works for LLMs
The job here is summarizing, not reasoning. That distinction matters. Models are good at pulling the three most important points out of an article and grabbing the quote that earns its place. They're much worse at drawing novel conclusions, and Betterpost never asks them to.
The pipeline fans out across hundreds of articles, runs them through summarizer prompts, and uses different models for different steps to keep cost and time in check. Each summary gets embedded, and those embeddings are scored against a fanned-out list of relevance criteria. So when you ask Betterpost for a newsletter on a subject, it's matching the freshest, most on-topic material before it writes a word.
By the way, Claude wrote all the prompts. I haven't written one by hand in a year.
A year of learning to write like a human
Betterpost has also spent a year figuring out how to write in a way that doesn't read as typical AI. This isn't about disguise. The output is openly machine-assisted. It's about being light, interesting, and human instead of the usual flat summarizer voice.
All of it is adjustable, and projects are unlimited. If you have a newsletter or a blog post you want to send, point Betterpost at it.
The UI is gone
When I built this for myself, it had an extensive interface: tables, technical readouts, all the knobs I wanted. That's all gone now.
Betterpost is entirely an MCP server. You add it to your favorite chatbot, name the project you want to make content for, and go. No dashboard, no menu. It genuinely feels like magic, which is a strange thing to say about a tool I understand the internals of completely.
The SpaceMolt merch store is going live soon, and every design in it was generated by AI. I'm strongly against passing off generative art as human-made, so I want to be clear up front: SpaceMolt is a game both powered and played by AI, so an AI-generated merch store is the honest version of the thing, not a shortcut around a person.
The whole store came out of a week of pointing Claude at Nano Banana Pro (Gemini 3 Pro Image) and getting out of the way. I never wrote a prompt file or a style guide. Claude built the prompt system, built the generation tools, built a review app, and drove the image model underneath the whole time.
Two people wearing black graphic t-shirts with metallic sci-fi artwork against a dark teal starfield background
The week, roughly in order
The starting point was themes. Claude (Opus, sometimes Fable) generated art directions per empire, using our existing art style and the lore we'd already written for each faction: the Voidborn Collective, the Crimson Pact, and the rest.
On a whim I asked for 20 logo ideas. One was genuinely good: a crescent moon with a ring, where the crescent reads as vaguely claw-shaped. We've been trying to de-crustacean the project now that the MoltBook meme has mostly run its course, but a few subtle nods survive, and this was one worth keeping.
Then came 150 merch ideas: stickers, t-shirts, enamel pins. I told Claude to study one of my favorite shirts, a vintage Blade Runner tee, for the shirt designs. I also pointed it at our data warehouse of player actions (Parquet on Cloudflare R2) to mine for in-jokes.
The review tool
150 ideas is too many to eyeball in a folder, so Claude built a review app: every piece of art with keep, discard, or needs-review buttons and a notes field.
Merchandise review interface showing eight heavy metal band t-shirt designs with scores, descriptions, and keep/discard voting options
The prompts driving the shirt mockups were absurdly specific, because they had to be. Here's a slice of one, generating a photo of a model wearing a design that already existed as a flat print:
IMAGE 1 is the exact, finished t-shirt print design, a heavy-metal band-tee graphic: a spiked chrome "SPACEMOLT" logotype at the top, below it a colossal claw-crescent in scarred chrome wreathed in teal lightning, hooking through a cracking rocky planet surface at the bottom.
TASK: Generate a PHOTOREALISTIC editorial/studio photograph of a model wearing a black cotton t-shirt printed with EXACTLY this design as a chest print. Reproduce the print faithfully: every letter spelled correctly (SPACEMOLT, exactly nine letters: S-P-A-C-E-M-O-L-T, one word), the artwork copied stroke for stroke, do NOT redesign, reinterpret, simplify, or add elements. The print should look like real screen-printed ink on fabric: it follows the drape and folds of the shirt, with subtle fabric texture showing through, no floating or pasted-on graphic.
What survived
The keepers are better than I expected. A "Planetbreaker" tee that looks like the coolest 80s British metal shirt ever made. A "Died with 0 credits" sticker, which is an in-joke about players stranding themselves in remote systems with no money to buy their way out. And an enamel pin of Molty, our AI head of growth.
SpaceMolt Supply Depot merchandise page displaying eight sticker designs with prices and descriptions on dark background
The logo problem
The most interesting moment was Claude re-teaching itself how to keep the logo consistent across every piece. It kept trying to describe the logo in prose, and the model kept drawing it wrong. I had Claude write down what fixed it and save it as a rule so it wouldn't repeat the mistake:
A logo cannot be described, attach it. Thirty designs drew the icon wrong from prose; one reference image fixed it. But say it's a silhouette reference only, or its palette and framing leak in.
That last part is the subtle bit. Hand the model a reference image and it will happily absorb the reference's color and composition too, unless you tell it the image is a shape and nothing else.
