This makes no sense to me. I often leave sessions idle for hours or days and use the capability to pick it back up with full context and power.

The default thinking level seems more forgivable, but the churn in system prompts is something I'll need to figure out how to intentionally choose a refresh cycle.

Any software you use with this clause, "THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."

Already attests that the authors do not offer guarantees that the software will have the features you need, supply chain security or otherwise.

From a narrative standpoint, I agree it makes more sense to focus on a duplicated lookup table and fastest wins, however, from an engineering standpoint, framing it in terms of channel de-correlated reads has more possibilities. For example, if you need to evaluate multiple parallel ML models to get a result then by intentionally partitioning your models by channel you could ensure that a model does reads on only fast data or only slow data. ML models might not be that interesting since they are good candidates for being resident in L3.

Just figured you'd want to know anytime soon was at least a year ago.

There are a few challenges here. - Off-cpu is missing the interrupt with integrated collection of stack traces, so you instrument a full timeline when they move on and off cpu or periodically walk every thread for its stack trace - Applications have many idle threads and waiting for IO is a common threadpool case, so its more challenging to associate the thread waiting for a pool doing delegated IO from idle worker pool threads

Some solutions: - Ive used nsight systems for non GPU stuff to visualize off CPU time equally with on CPU time - gdb thread apply all bt is slow but does full call stack walking. In python, we have py-spy dump for supported interpreters - Remember that any thing you can represent as call stacks and integers can be converted easily to a flamegraph. eg taking strace durations by tid and maybe fd and aggregating to a flamegraph

Pytorch multiprocessing queues work this way, but it is hard for the sender to ensure the data is already in shared memory, so it often has a copy. It is also common for buffers to not be reused, so that can end up a bottleneck, but it can, in principle, be limited by the rate of sending fds.

The issue I was referring to is that in Javascript, you can write code which uses multiple versions of the same library which are mutually incompatible. Since they're mutually incompatible, no sat-solve or unifyer is going to help you. You must permit multiple versions of the same library in the same environment. So far, my approach of ignoring some Javascript libraries has worked for my backend development. :)

I think you can fix these issues by using a package manager around Bazel. Conda is my preferred choice because it is in the top tier for adoption, cross platform support, and supported more locked down use cases like going through mirrors, not having root, not controlling file paths, etc. What Bazel gets from this is a generic solution for package management with better version solving for build rules, source dependencies and binary dependencies. By sourcing binary deps from conda forge, you get a midpoint between deep investment into Bazel and binaries with unknown provenance which allows you to incrementally move to source as appropriate.

Additional notes: some requirements limit utility and approach being partial support of a platform. If you require root on Linux, wsl on Windows, have frequent compilation breakage on darwin, or neglect Windows file paths, your cross platform support is partial in my book.

Use of Java for Bazel and Python for conda might be regrettable, but not bad enough to warrant moving down the list of adoption and in my experience there is vastly more Bazel out there than Buck or other competitors. Similarly, you want to see some adoption from Haskell, Rust, Julia, Golang, Python, C++, etc.

JavaScript is thorny. You really don't want to have to deal with multiple versions of the same library with compiled languages, but you have to with JavaScript. I haven't seen too much demand for JavaScript bindings to C++ wrappers around a Rust core that uses C core libraries, but I do see that for Python bindings.

In my experience, LLMs are useful primarily as rubber ducks on complex problems and rarely useful as code generation for such.

Instead, I spend most of my time between the interesting work doing rote work which is preventing me from getting to the essential complexity, which is where LLM code gen does better. How do I generate a heat map in Python with a different color scheme? How do I parse some logs to understand our locking behavior? What flags do I pass to tshark to get my desired output?

So, I spend less time coding the above and more time coding how we should redo our data layout for more reuse.

I guess since you said header, you keep everything opaque and create a header for that which gets translated to Zig.

Promiscuous doesn't have to mean having a low tolerance for difficulty, but everything else you wrote seems to support that. So, are you saying that enduring difficulty is unnecessary, or did you mean something different?

I was surprised it wasn't combined with clangd.

Comments URL: https://news.ycombinator.com/item?id=37586518

Points: 58

# Comments: 16

Whether something is too short depends on your context. requests.get might be ambiguous for someone who has never seen the code base before, but will quickly get obviously with a little exposure, so does the code base have dedicated maintainers?

The skill of good naming isn't distributed evenly and OP's names are pretty good, so I'd be happy for them to come along to my code base and rename things as long as it was within other constraints (not consuming too much time, doesn't break api, etc).

Like you said, tensor core is similar to a special purpose ALU and is at a lower level of abstraction than something with an instruction pointer.