关于Show HN，不同的路径和策略各有优劣。我们从实际效果、成本、可行性等角度进行了全面比较分析。
维度一：技术层面 — Tokenizer and Inference Optimization
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维度二：成本分析 — Hey folks! We're two months into the year and I'd like to cover all of the progress that's been made on jank so far. Before I do that, I want to say thank you to all of my Github sponsors, as well as Clojurists Together for sponsoring this whole year of jank's development!jank bookTo kick things off, let me introduce the jank book. This will be the recommended and official place for people to learn jank and its related tooling. It's currently targeted at existing Clojure devs, but that will start to shift as jank matures and I begin to target existing native devs as well. The jank book is written by me, not an LLM. If you spot any issues, or have any feedback, please do create a Github Discussion.My goals for this book include:Introduce jank's syntax and semanticsIntroduce jank's toolingWalk through some small projects, start to finishDemonstrate common use cases, such as importing native libs, shipping AOT artifacts, etc.Show how to troubleshoot jank and its programs, as well as where to get helpProvide a reference for error messagesAs the name and technology choice implies, the jank book is heavily inspired by the Rust book.Alpha statusjank's switch to alpha in January was quiet. There were a few announcements made by others, who saw the commits come through, or who found the jank book before I started sharing it. However, I didn't make a big announcement myself since I wanted to check off a few more boxes before getting the spotlight again. In particular, I spent about six weeks, at the end of 2025 and into January, fixing pre-mature garbage collection issues. These weeks will be seared into my memory for all of my days, but the great news is that all of the issues have now been fixed. jank is more and more stable every day, as each new issue improves our test suite.LLVM 22On the tail of the garbage collection marathon, the eagerly awaited LLVM 22 release happened. We had been waiting for LLVM 22 to ship for several months, since it would be the first LLVM version which would have all of jank's required changes upstreamed. The goal was that this would allow us to stop vendoring our own Clang/LLVM with jank and instead rely on getting it from package managers. This would make jank easier to distribute and, crucially, make jank-compiled AOT programs easier to distribute. You can likely tell from my wording that this isn't how things went. LLVM 22 arrived with a couple of issues.Firstly, some data which we use for very important things like loading object files, adding LLVM IR modules to the JIT runtime, interning symbols, etc was changed to be private. This can happen because the C++ API for Clang/LLVM is not considered a public API and thus is not given any stability guarantees. I have been in discussions with both Clang and LLVM devs to address these issues. They are aware of jank and want to support our use cases, but we will need to codify some of our expectations in upstreamed Clang/LLVM tests so that they are less likely to be broken in the future.Secondly, upon upgrading to LLVM 22, I found two different performance regressions which basically rendered debug builds of jank unusable on Linux (here and here). Our startup time for jank debug builds went from 1 second to 1 minute and 16 seconds. The way jank works is quite unique. This is what allows us to achieve unprecedented C++ interop, but it also stresses Clang/LLVM in ways which are not always well supported. I have been working with the relevant devs to get these issues fixed, but the sad truth is that the fixes won't make it into LLVM 22. That means we'll need to wait several more months for LLVM 23 before we can rely on distro packages which don't have this issue.That's a tough pill to swallow, so I took a week or so to rework the way we do codegen and JIT compilation. I've not only optimized our approach, but I've also specifically crafted our codegen to avoid these slower parts of LLVM. This not only brings us back to previous speeds, it makes jank faster than it was before. Once LLVM 23 lands, the fixes for those issues will optimize things further.So, if you've been wondering why I've been quiet these past few months, I likely had my head buried deep into one of these problems. However, with these issues out of the way, let's cover all of the other cool stuff that's been implemented.nREPL serverjank has an nREPL server now! You can read about it in the relevant jank book chapter. One of the coolest parts of the nREPL server is that it's written in jank and yet also baked into jank, thanks to our two-phase build process. The nREPL server has been tested with both NeoVim/Conjure and Emacs/CIDER. There's a lot we can do to improve it, going forward, but it works.As Clojure devs know, REPL-based development is revolutionary. To see jank's seamless C++ interop combined with the tight iteration loop of nREPL is beautiful. Here's a quote from an early jank nREPL adopter, Matthew Perry:The new nREPL is crazy fun to play around with. Works seamlessly with my editor (NeoVim + Conjure). It's hard to describe the experience of compiling C++ code interactively - I'm so used to long edit-compile-run loops and debuggers that it feels disorienting (in a good way!)A huge shout out to Kyle Cesare, who originally wrote jank's nREPL server back in August 2025. Thank you for your pioneering! If you're interested in helping out in this space, there's still so much to explore, so jump on in.C++ interop improvementsMost of my other work on jank has been related to improving C++ interop.Referred globalsjank now allows for C/C++ includes to be a part of the ns macro. It also follows ClojureScript's design for :refer-global, to bring native symbols into the current namespace. Without this, the symbols can still be accessed via the special cpp/ namespace.(ns foo，推荐阅读易歪歪获取更多信息
根据第三方评估报告，相关行业的投入产出比正持续优化，运营效率较去年同期提升显著。。查啦对此有专业解读
维度三：用户体验 — The sites are slop; slapdash imitations pieced together with the help of so-called “Large Language Models” (LLMs). The closer you look at them, the stranger they appear, full of vague, repetitive claims, outright false information, and plenty of unattributed (stolen) art. This is what LLMs are best at: quickly fabricating plausible simulacra of real objects to mislead the unwary. It is no surprise that the same people who have total contempt for authorship find LLMs useful; every LLM and generative model today is constructed by consuming almost unimaginably massive quantities of human creative work- writing, drawings, code, music- and then regurgitating them piecemeal without attribution, just different enough to hide where it came from (usually). LLMs are sharp tools in the hands of plagiarists, con-men, spammers, and everyone who believes that creative expression is worthless. People who extract from the world instead of contributing to it.
维度四：市场表现 — It’s possible that artificial intelligence is something unique in human history, but the mass automation it seems bound to produce definitely isn’t.
随着Show HN领域的不断深化发展，我们有理由相信，未来将涌现出更多创新成果和发展机遇。感谢您的阅读，欢迎持续关注后续报道。