JIT breakthroughs squeeze latency from microseconds to minutes

The community just proved you can compile a snippet of code in five microseconds and run it instantly, a milestone that forces every latency‑obsessed stack to rethink its build pipeline. Malisper’s blog post, “JIT Compiling Code in 5μs,” shows a minimal example that hits the 5 µs mark on a commodity laptop, turning a naïve JIT into a practical latency hack.

The post, which surfaced on Hacker News and earned 123 points and 78 comments, includes a link to the full source on https://malisper.me/jit-compiling-code-in-5-us/.[^1]¹²³ The author walks through a tiny code generator that emits x86‑64 machine code, maps it into an executable page, and calls it directly from Python.⁴ No external compiler, no file I/O, just a handful of syscalls.⁴ The result is a function that adds two integers in under a hundred nanoseconds, and the JIT overhead itself stays under five microseconds.⁴⁵ The demonstration is deliberately stripped down, but the timing numbers are real and reproducible on the author’s hardware.⁴

Across the AI frontier, the A1 project is turning the same JIT philosophy toward large‑language‑model agents.⁶ A1’s “agent compiler” accepts a description of tools and a task, then emits either an ahead‑of‑time binary or a just‑in‑time routine tuned to the specific input.⁶ The framework claims to replace the ubiquitous LangChain and aisdk stacks, which currently run agents inside a static while‑loop interpreter.⁶ Those loops are described as “slow, unsafe, and highly nondeterministic,” a pain point that A1’s compiler addresses by generating super‑optimal execution plans under engineered constraints like type safety.⁶

A1’s ultimate goal, according to its brief description, is “determinism‑maxing”: encode as much of the workflow as deterministic code (100 % accuracy) and reduce LLM calls to the bare minimum. The repository ships a tests/ directory with dozens of examples, and the team promises documentation at docs.a1project.org and a forthcoming paper.⁶ The project explicitly recommends itself for latency‑critical tasks, for processing untrusted data, or for any scenario that may need to run generated code on the fly.⁶

Even hobbyists are getting in on the act. A Hacker News post titled “JIT for Dummies: JIT Compiling RPN in Python” walks readers through a 50‑line proof‑of‑concept that turns a reverse‑Polish‑notation calculator into native x86 instructions.⁷ The author maps the RPN stack onto two registers, eax and ecx, and uses Python’s ctypes facilities to allocate an executable buffer, copy the machine code, and invoke it.⁷ The write‑up includes a whimsical shoe‑analogy that eventually collapses into a concrete register‑swap algorithm: push the current ecx to the real stack, swap eax and ecx, then load the next literal into eax.⁷ Binary operators pop the two registers, compute inline, and push the result back, keeping the most recent operand in eax.⁷

The post also flags a security caveat: modern operating systems enforce DEP (Data Execution Prevention), which blocks execution of writable memory.⁷ The author sidesteps this by marking the buffer as executable via mprotect‑style calls, a technique that works on most Unix‑like kernels but would raise eyebrows in a hardened production environment.⁷ Still, the example proves that a high‑level language can generate and run low‑level code without dropping into C, a pattern that mirrors the micro‑benchmark in Malisper’s five‑microsecond demo.⁴⁷

On the Java side of the spectrum, an experimental Gradle plugin brings the Elide native runtime into the build process. Elide, a GraalVM‑based native image, bundles a full Java/Kotlin/Python/JS toolchain and can act as a drop‑in replacement for javac. The plugin rewrites JavaCompile tasks to invoke elide javac via a shim, effectively skipping the JVM warm‑up phase that typically dominates compile latency.

According to the plugin’s README, projects with fewer than 10 000 classes have reported compile speedups of “up to 20x faster than stock javac.” The performance gain comes from two sources: Elide’s native image eliminates JIT warm‑up, and its dependency resolver uses a lockfile to fetch Maven artifacts in a single native pass. The plugin is marked beta and experimental, and the authors warn users to “use at your own risk” while encouraging bug reports via the usual GitHub channels.

Elide’s broader ambition is to become a “batteries‑included” runtime for multiple languages, but the Gradle integration shows a concrete, immediate benefit for Java teams that care about build times. By replacing the traditional JDK toolchain with a native image, teams can shave minutes off nightly builds, a saving that compounds across large CI pipelines.

These four efforts—microsecond JITs, AI agent compilers, Python‑based register tricks, and native Java toolchains—share a common thread: they all treat compilation as a runtime operation rather than a separate, offline step. The shift blurs the line between interpreter and compiler, allowing developers to generate code on demand, tailor it to the exact data at hand, and execute it with minimal overhead. The trade‑off is increased complexity in the build stack and a heightened security surface, especially when executable memory is allocated on the fly.

What to watch: The next few months will reveal whether these prototypes mature into production‑ready components. Keep an eye on the A1 project’s upcoming paper for formal performance numbers and on the Elide plugin’s GitHub issues for real‑world stability reports. If the five‑microsecond JIT benchmark gains traction, we may see language runtimes expose low‑level JIT APIs as first‑class features. The key metric to track will be end‑to‑end latency for latency‑critical pipelines—whether that’s a chat‑bot responding in sub‑second time or a CI system finishing a full build in under five minutes.