DEV Community: ninghonggang

I Stopped Picking Between Cursor and Claude Code

ninghonggang — Wed, 03 Jun 2026 11:04:59 +0000

I was the guy who kept switching between Cursor and Claude Code every other week. For about a month I'd live in one, hit a wall, jump to the other, repeat. Honestly it was exhausting and my PR throughput was garbage.

Then I read a 2026 review on Juejin that put hard numbers on it. 48 junior devs at JR Academy in Australia were forced to use AI coding tools for 12 months, full commit logs tracked. The author reported median spend around $65 a month on a Cursor plus Claude Code 80/20 split, with PR turnaround dropping by roughly 1.1 days per PR. Back of the napkin that's something like a 100x ROI against a junior salary, though I haven't verified their methodology myself so take the multiplier with a grain of salt.

What I actually took from the piece is the 80/20 framing. Cursor stays in the editor doing Tab completion, quick refactors, that sort of in-flow stuff where the latency matters. Claude Code gets spun up in the terminal for the heavier jobs, cross-file refactors, prod incident debugging, the tasks where it will happily burn through tokens until the work is done. I used to feel guilty about burning context. I don't anymore. The cost is small compared to the time I get back, and Claude Code 1.0 from the Anthropic Cookbooks repo has gotten noticeably better at not spiraling on big prompts.

One thing the Juejin post mentioned that I want to flag honestly. Trae from ByteDance apparently lags pretty hard. I haven't tried it in a couple months so maybe it's caught up, but the consensus over there was that it wasn't even close to Cursor or Claude Code. If you're considering the third option, I'd just skip it for now.

I'll probably keep running this combo through the rest of the year and see if the gains hold up. If something new from OpenAI or Google disrupts the terminal-agent space I might rethink, but right now this is the only setup that hasn't made me feel like I'm fighting the tool.

I Used Cursor, Claude Code and Codex for Six Months — Here Is What Actually Stuck

ninghonggang — Tue, 02 Jun 2026 11:03:55 +0000

I've been using Cursor, Claude Code, and OpenAI Codex together for about half a year on a BIM product, and the question I keep getting is which one is "best." After switching between them way more times than I'd like to admit, I've come to think the question itself is wrong. They're three different answers to three different problems, and treating them as competitors just makes you frustrated.

Here's roughly how I split the work. Cursor lives inside VS Code and its Tab model is the only one I trust to keep me in flow when I'm writing new code. I press Tab, it predicts the next block, I keep going. Claude Code runs in my terminal and it's where I send the messy stuff, refactors that span six files, plans I want written down before anyone touches anything, anything where I need a 1M token context. Codex is the one I throw parallel jobs at. Last Tuesday I queued up five cleanup tasks and walked away for twenty minutes. That part genuinely works, and I haven't found a way to replicate it in Cursor or Claude Code.

The combo that finally stopped feeling wasteful costs me around $120 a month, Claude Code pro plus Codex pro. I let Claude Code write the analysis and the plan, I hand it to Codex to execute, and Cursor handles the small in-the-moment edits that I can't stand doing by hand. The Hooks in Claude Code also catch lint and test failures before anything gets committed, which is the real reason I stopped trusting "AI wrote this PR" without a check.

That said, I'm not sure this combination is right for everyone. A solo developer paying $40 a month with Cursor Pro and Claude Code pro is probably in better shape than I am for the first six months. And the Codex PRs can be wildly inconsistent, sometimes clean enough to merge, sometimes nonsense that I have to rewrite from scratch, so I'd budget real review time. There's also a chance the whole toolchain looks different in a year. For now though, the hammer-screwdriver analogy is the only one that matches what I've actually felt using them.

AI Coding Tools进化论：我观察到的2025年实际格局

ninghonggang — Mon, 01 Jun 2026 11:03:36 +0000

I have been using AI coding tools professionally for about three years now, and honestly, these things have gotten weirdly good. Looking at the latest rankings, it is clear the landscape has shifted dramatically in ways I did not expect.

Here is what stands out to me: Claude 3.7 Sonnet from Anthropic is hitting 91.2% on HumanEval for programming tasks. That is insane when you consider where these tools started. I first tried early versions of GitHub Copilot back in 2022, and the suggestions were... let us just say I double-checked everything. Now I am seeing long-context reasoning that actually understands architectural patterns across thousands of lines of code.

But here is the thing nobody talks about: the ranking does not tell you which tool fits your workflow. I have watched teams jump ship to the best model only to realize their use case does not align with its strengths. Some models excel at rapid prototyping, others at careful reasoning. I have not personally stress-tested every combo, so take this with a grain of salt.

The other day I was debugging a gnarly race condition with Cursor, and the context window actually traced through multiple interconnected files to explain the bug. That was genuinely impressive. Meanwhile, DeepSeek R1 has been making waves in the Chinese market with apparently strong reasoning speed, though I have not verified those claims myself.

What is interesting is the shift from pure benchmark scores to ecosystem play. Microsoft and Google is enterprise integration has become a real differentiator. Teams already in the Microsoft ecosystem often find Copilot integration smoother, while startups seem to gravitate toward Claude for its thoughtful approach.

Honestly? I think we are past the point of which AI is best questions. The practical answer is workflow-dependent, and the tools are all good enough now that personal preference and team fit matter more than marginal benchmark differences. That said, I will probably keep experimenting — these things evolve so fast that today is runner-up might be tomorrow is leader.

My 2025 Take on AI Coding Tools: Claude Code vs Cursor

ninghonggang — Sun, 31 May 2026 11:03:35 +0000

I have been watching the AI coding tool space pretty closely this year, and honestly, it has been a wild ride. If you are still deciding between Claude Code and Cursor, let me share what I have observed from my own usage and the endless debates in the community.

Claude Code surprised me. It is not just another copilot—it is more like having an actual engineer who can plan, execute, and verify code autonomously. The context window is massive (up to 1M tokens with Sonnet), and it genuinely understands your entire codebase. But here is the thing: token consumption is real. Running Opus 4 for serious work will burn through your budget fast. That is why many devs switched to pairing Claude Code with domestic models like Doubao, GLM, or MiniMax by year-end. The domestic models are not as polished, but they are cheap and iterate quickly.

Cursor still appeals to developers who want that IDE-based experience with real-time code completion. The feedback loop is tighter—you see suggestions as you type. Composer 2.0 helps with multi-file edits. However, the subscription changes Anthropic made to prevent direct model access rubbed a lot of users the wrong way. The rate limits and "you cannot use Claude anymore" situation left a bad taste.

For 2026, my take is domestic tools will dominate the Chinese market simply because of pricing and compliance. Whether Western devs follow that trend, I honestly do not know. Right now I am using Claude Code with GLM 4 for my daily driver. It gives me the best balance between capability and cost.

I've been keeping an eye on the AI ranking data from 2025, and honestly, there's some stuff worth chatting about after looking a

ninghonggang — Sat, 30 May 2026 11:03:35 +0000

First off, ChatGPT is still dominating with around 5.5 billion visits, but the growth has slowed to just 6.82%. Meanwhile, Gemini is going crazy with 28.9% growth - that's huge. Google pushed hard on the multimodal thing, and apparently it's working. I'm seeing more people switch to Gemini for projects that need image handling, though I haven't personally tested it in production yet.

What really caught my attention is DeepSeek R1. It's sitting at rank 4 on the global list and it's actually got some decent traction. China's been doing interesting work in the reasoning model space, and this one's reportedly faster than older versions - maybe 3x improvement according to some benchmarks I stumbled across. The Chinese market is obviously huge, but whether it scales internationally is something I'm curious about. No idea if it'll stick for long-term use.

And oh, Claude 3.7 Sonnet - the programming scores are wild at 91.2 on HumanEval. That number matters if you're actually shipping code. I've seen developers specifically choose it for longer documents (100k tokens is no joke), and the safety compliance side is stronger than some alternatives. Not perfect, but solid.

The honest take? We're definitely past the "try everything" phase. People are settling into their preferred tools now. The ones with real improvements in speed and specific use cases are the ones still growing. The rest are stagnating - there's your signal right there.

One thing though - these numbers shift monthly. Some rankings from March might look totally different now. Worth checking back if you're tracking this stuff for work.

I Switched from Cursor to Claude Code for Code Reviews – Here's What Actually Changed

ninghonggang — Fri, 29 May 2026 11:03:00 +0000

I've been using Cursor daily for about eight months now. It's become my go-to for writing code fast – the Tab completion feels like reading my mind, and the Agent mode handles messy refactoring tasks that would've taken me hours.

But honestly? I kept hitting a wall with code reviews. Cursor's fine for generating code, but when I needed to understand a 3000-line codebase I hadn't touched before, it just couldn't keep up. That's when I dove into Claude Code.

The big difference I noticed: Claude Code's 1M token context window means I can feed it an entire module at once. No chopping files, no context loss. For reviews, that's gold. Last week I ran claude review --since="1 week ago" on our main branch and got a summary that actually flagged three potential bugs I'd missed. Not just style nits – real_logic issues.

That said, Claude Code isn't replacing Cursor for me anytime soon. Writing code with Cursor's composer still feels faster for greenfield work. The UI is just built for it – select a chunk, ask AI to refactor, done.

My take: these tools aren't competing. They're solving different problems. I use Cursor to build, Claude Code to review and debug. Both are free and work great on macOS. Worth trying if you're serious about shipping cleaner code.

My Honest Take on AI Coding Tools in 2025 After Using Them in Production

ninghonggang — Thu, 28 May 2026 11:03:31 +0000

I've been watching the AI coding tool space blow up since 2023, and honestly it's been a wild ride. Every few months there's a new tool promising to replace your keyboard entirely, and every few months I end up right back where I started.

So here's my take on the 2025 landscape, based on what I've actually used in production over the past few months - not just hype.

Cursor's the elephant in the room. They've reportedly hit $500 million in ARR, which is wild considering they launched as a fork of VS Code like four years ago. What keeps me coming back isn't the flashy features - it's the consistency. When I need a whole file rewritten, it just works. The agent mode will read, edit, and run tests in one go without me babysitting it. That being said, I'll admit the free tier gets limiting fast, and if you're not comfortable with the terminal you'll probably get frustrated.

Then there's v0 from Vercel. It's the opposite end of the spectrum - you paste a prompt, it spits out a React component, you tweak with natural language. No CLI, no config, just magic until it isn't. For prototypes it's unbeatable, but I've hit walls when I need real backend logic. Your options get pretty narrow pretty quickly.

I'm less sold on Claude Code right now. Anthropic built it, and the model underneath is solid, but compared to Cursor it feels like they're shipping updates in slow motion. Bugs stick around for months. The integrations aren't there yet either, though maybe I'm expecting too much.

The interesting one to watch is Codex from OpenAI. They're iterate fast, and the underlying model is genuinely strong. Give it six months and this could look very different.

My advice? Don't marry any tool. The space moves too fast. Pick one that fits your workflow today, and reassess in three months. That's what I'm doing.

I Tried Letta – Giving AI Agents Real Memory

ninghonggang — Wed, 27 May 2026 11:04:25 +0000

I've been playing around with Letta lately — it's that open-source memory engine for AI agents that's been getting some traction on GitHub. The idea is pretty simple: give your AI agent persistent memory so it doesn't forget everything every time you restart it.

Most AI demo projects have this fundamental problem — they're flashy for five minutes, then you restart the process and it's like talking to someone with dementia. Letta tries to solve that by letting agents store and retrieve memories across sessions.

I set it up with pip install letta-client, grabbed an API key from their dashboard, and had a basic agent with memory working in about 15 minutes. You define what the agent should "remember" — facts about your preferences, ongoing projects, whatever. Then when you chat with it next time, it pulls relevant context and you're not starting from zero.

The sweet spot seems to be tasks that span multiple conversations. I've got one agent that helps me track coding projects across a few weeks — it remembers which libraries I used, what failed during testing, the usual stuff that you'd otherwise lose in Slack messages or random text files.

Not gonna lie, though, the latency hit can be annoying when fetching older memories. It's noticeable enough that I've caught myself waiting a second or two, which sounds small but breaks the flow. And the admin interface could use some work — it's functional but definitely early-stage tech.

I haven't stress-tested it over months yet, so I can't say whether the memories degrade or get noisy over time. My guess is they'd benefit from periodic cleanup, like defragmenting a hard drive, but that's just speculation. If you're building anything beyond one-off Q&A chatbots, this is more promising than stuffing everything into the system prompt.

Wait, OpenAI Finally Went Open-Source. Here is What Actually Different

ninghonggang — Tue, 26 May 2026 11:03:42 +0000

I cannot believe I am saying this, but OpenAI actually went and open-sourced a model. After years of keeping everything behind API walls, they dropped GPT-OSS-120B on us in August with Apache 2.0 license. Pretty wild when you think about it.

The thing is, this is not some toy model they threw over the fence either. It benchmarks close to o4-mini, which is their own reasoning model. Input costs around 0.8 yuan per million tokens — that is dirt cheap compared to what you would pay for equivalent capability through their API. You can run it locally, fine-tune it, throw it in products. Commercial use included.

But here is where it gets interesting. Running a 120B parameter model locally is not something most people can just do on their laptop. You would need serious GPU hardware, probably 8x H100s or something in that neighborhood. The inference performance is close to o4-mini, sure, but you are looking at a significant infra investment to actually host it yourself.

I have not personally benchmarked it against running the same workload through OpenAI API yet. My gut says for most teams, the API route might still make more sense financially when you factor in the operational overhead of self-hosting. But the option is there now, and that is a meaningful shift for the ecosystem.

What is worth noting is the timing. They announced this alongside an $8 billion financing round at $300 billion valuation. Mix of PR play and genuine strategic move — they are betting that opening up the weights creates an ecosystem lock-in even if someone else matches their closed products eventually.

The open-source LLM space is getting crowded though. Meta LLaMA has been there, Mistral has been pushing releases, and now this. For practitioners, it is good trouble we are having here — more choices, more competitive pricing, more ability to self-host. I just hope they keep iterating on it rather than dropping it and walking away like some companies do.

This is genuinely worth keeping an eye on if you have been waiting for a legitimate OpenAI-backed open weights model to experiment with.

I Used Cursor for 3 Months - Here is What Changed

ninghonggang — Mon, 25 May 2026 11:04:48 +0000

I have been playing with Cursor for about three months now, and honestly, it is starting to feel like my IDE cannot live without it. The autocomplete is solid, but the real magic is how it understands your whole project context - not just the file you are staring at.

Looking at the 2025 AI tool rankings though, I notice that coding assistants are barely showing up. ChatGPT dominates with nearly 55 billion visits, and it is mostly being used for chat, not code. Perplexity is in there as the search alternative. But for actual programming? The numbers are much smaller.

What is interesting is seeing which tools actually stuck around. GitHub Copilot has been out for years, but these newer entrants like Cursor and Claude Code feel different. Cursor integration with Claude model means it can actually follow multi-file refactoring across your whole codebase - I have watched it rename components across React projects without breaking imports, which still blows my mind a little.

That said, I have not stress-tested everything. My day job is not ship emoji-level traffic, so take that with a grain of salt. And there is a learning curve - prompts that sound natural do not always get you what you expect. Sometimes being overly specific helps, sometimes being vague gets better results. Go figure.

The real question is whether these tools are making developers better or just different. I have noticed I write code faster but sometimes skip the mental why that used to happen during manual typing. That is probably a net win for most work, but I will reserve judgment until I have used it longer.

I've been spending way too much time testing AI coding tools lately. First got hooked on Cursor, then gave Claude Code a shot...

ninghonggang — Sun, 24 May 2026 11:03:35 +0000

The vibe with Cursor feels different. It's got that VS Code DNA underneath, so switching from a regular editor is basically frictionless. The tab completion actually works pretty well most of the time—when it guesses right, it really feels like magic. The Agent mode is where things get interesting, though sometimes it goes off rails and writes code I definitely didn't ask for.

Claude Code is more of a thinking tool since it runs locally through a CLI with a proper agent loop. It feels slower when responding, more deliberate, less rush-y. The tradeoff is you're staring at your terminal the whole time, no fancy UI, which some folks might not love. I mostly use it for the harder problems—the ones where I need the reasoning right before jumping into code. Haven't tried it on huge projects yet though, so maybe I'm missing something.

For my daily use, I'll probably stick with Cursor. That tight editor integration is just too convenient. But hey, maybe it's just muscle memory at this point—my teammate swears by Claude Code and produces solid code. Hard to declare a winner here, honestly. They each appeal to different workflows.

After 6 Months with Cursor and Claude Code: My Honest Take

ninghonggang — Fri, 22 May 2026 11:03:02 +0000

I've been using AI coding assistants since the early Copilot days, and honestly, 2025 feels like the year things finally got serious. The jump from basic autocomplete to genuine pair programming is honestly kind of wild when you think about it.

My take after shipping production code with both Cursor and Claude Code for the past few months: they're actually targeting slightly different workflows despite the surface-level overlap. Cursor feels faster out of the box—the Tab key flow just works for quick iterations. Whole-line completions hit the mark maybe 70-80% of the time for stuff I've written before, which makes boilerplate refactoring feel almost effortless. The inline chat (Cmd+K) is surprisingly well-integrated for a round-the-clock code review without breaking my mental flow.

Claude Code clicked for me differently. The real difference wasn't the completion quality—it was the ability to hand off genuinely ambiguous problems. Last week I had a nasty race condition in a Python async codebase that I'd been staring at for hours. Instead of explaining the whole context, I just pasted three files and said "this is failing intermittently, I think it's in the connection pool logic." It spent maybe thirty seconds thinking, then walked me through its hypothesis, showed me exactly where the cleanup was happening in the wrong order, and proposed a fix that was cleaner than my original implementation. That's the moment you realize these tools are becoming actual teammates rather than fancy autocomplete.

The tradeoff is speed versus depth. Cursor wins for "let me bang this out fast" moments—I've finished API glue code while literally thinking about dinner. Claude Code wins when I need someone to think alongside me, even if it takes an extra fifteen seconds. Neither is strictly better.

I'm honestly not sure how long this distinction lasts—both companies are moving fast. Anthropic and Cursor are probably watching each other's usage patterns closely. My gut says within a year they'll converge more than they differ now. But for right now, if you're doing a lot of greenfield feature work where speed matters most, Cursor might serve you better. If you're deep in legacy code that needs careful hands, Claude Code's reasoning quality is worth the slight slowdown.

Neither replacement for actually understanding your code, obviously. But the bar for "good enough" keeps moving higher.