emeryberger
Joined 12 years ago
Karma
276
Submissions
101
Member since
Nov 17
Recent submissions
Comment · 4 months ago
Since we are talking about Python type-checkers: we've built a (non-AI based) type assistant for Python called RightTyper (https://github.com/RightTyper/RightTyper). Below is a brief description; a technical paper describing RightTyper is here: https://arxiv.org/abs/2507.16051, "Getting Python Types Right with RightTyper"
RightTyper is a Python tool that automatically generates type annotations for your code. It monitors your program as it runs and records the types of function arguments, return values, local variables, and class fields — with only about 25% runtime overhead. This makes it easy to integrate into your existing tests and development workflow, and lets a type checker like mypy catch type mismatches in your code.
Comment · 1 year ago
RightTyper is much better in addition to running orders of magnitude faster.
https://github.com/RightTyper/RightTyper
(full disclosure, I am one of its authors).
Comment · 1 year ago
You can adjust the code on the page easily (it’s open source javascript) to determine the question you are after, which is a valid one: if an adversary starts today and adds x nodes to the existing network, what is their success rate?
BUT the author asked a different (but valid) question: assuming the adversary controls x out of N existing nodes, what is the success rate? I am unclear: is the assertion that everyone’s relay is honest today? From a privacy standpoint, that’s not a great assumption.
Comment · 1 year ago
The Tor Project web site makes a bold claim to its users:
"Tor Browser prevents someone watching your connection from knowing what websites you visit. All anyone monitoring your browsing habits can see is that you're using Tor."
Don't misinterpret this claim. It's not true that Tor protects you against "anyone monitoring your browsing".
On this web page, I provide you with the critical information missing from the Tor Project's website: if you estimate your adversary's resources in dollars, I'll estimated the probability that Tor will fail to protect you.
Comment · 1 year ago
Yes. It lets the LLM immediately obtain precise information rather than having to reason across the entire source code of the code base (which ChatDBG also enables). For example (from the paper, Section 4.6):
The second command, `definition`, prints the location and source
code for the definition corresponding to the first occurrence of a symbol on a
given line of code. For example, `definition polymorph.c:118` target prints the
location and source for the declaration of target corresponding to its use on
that line. The definition implementation
leverages the `clangd` language server, which supports source code queries via
JSON-RPC and Microsoft’s Language Server Protocol.Comment · 1 year ago
You should check out ChatDBG project - which AFAICT goes much further than this work, though in a different direction, and which, among other things, lets the LLM drive the debugging process - has been out since early 2023. We initially did a WinDBG integration but have since focused on lldb/gdb and pdb (the Python debugger), especially for Python notebooks. In particular, for native code, it integrates a language server to let the LLM easily find declarations and references to variables, for example. We spent considerable time developing an API that enabled the LLM to make the best use of the debugger’s capabilities. (It also is not limited to post mortem debugging). ChatDBG’s been out since 2023, though it has of course evolved since that time. Code is here [1] with some videos; it’s been downloaded north of 80K times to date. Our technical paper [2] will be presented at FSE (top software engineering conference) in June. Our evaluation shows that ChatDBG is on its own able to resolve many issues, and that with some slight nudging from humans, it is even more effective.
[1] https://github.com/plasma-umass/ChatDBG (north of 75K downloads to date) [2] https://arxiv.org/abs/2403.16354
Comment · 1 year ago
Nice UI. We started on a project that does this about 2 years ago. ChatDBG (https://github.com/plasma-umass/ChatDBG), downloaded about 70K times to date. It integrates into debuggers like `lldb`, `gdb`, and `pdb` (the Python debugger). For C/C++, it also leverages a language server, which makes a huge difference. You can also chat with it. We wrote a paper about it, should be published shortly in a major conference near you (https://arxiv.org/abs/2403.16354). One of the coolest things we found is that the LLM can leverage real-world knowledge to diagnose errors; for example, it successfully debugged a problem where the number of bootstrap samples was too low.
Comment · 1 year ago
Correction: it’s been a feature for at least two years. To our knowledge, Scalene was the first profiler to incorporate LLMs.
Comment · 1 year ago
One major limitation the LLM has is that it can't run a profiler on the code,
but we can. (This would be a fun thing to do in the future - feed the output
of perf to the LLM and say 'now optimize').
Comment · 1 year ago
Debuggers are great, and even better with LLMs!
https://github.com/plasma-umass/ChatDBG
https://arxiv.org/abs/2403.16354
Comment · 1 year ago
However, the resulting type annotations should work for Python 3.9+.
Comment · 1 year ago
It relies on some features introduced in Python 3.12, specifically the `sys.monitoring` API.
Comment · 1 year ago
Can’t honestly remember the details! But it’s very much an AWS-owned org.
Comment · 1 year ago
In addition to being a faculty member at UMass Amherst, I am an Amazon Scholar, working at Amazon Web Services, where this work was conducted.
Professor Berger has been a Visiting Scientist at Microsoft Research and at the Universitat Politècnica de Catalunya (UPC) / Barcelona Supercomputing Center (BSC). Professor Berger’s research spans programming languages, runtime systems, and operating systems, with a particular focus on systems that transparently improve reliability, security, and performance.
He and his group are the creators of a number of influential software systems including Hoard (http://www.hoard.org), a fast and scalable memory manager that accelerates multithreaded applications (used by companies including British Telecom, Cisco, Crédit Suisse, Reuters, Royal Bank of Canada, SAP, and Tata, and on which the Mac OS X memory manager is based); DieHard, an error-avoiding memory manager that directly influenced the design of the Windows 7 Fault-Tolerant Heap; and DieHarder, a secure memory manager that was an inspiration for hardening changes made to the Windows 8 heap.
He served two terms on the SIGPLAN Executive Committee, a decade as an Associate Editor for TOPLAS, and was the program chair of PLDI 2016 and co-program chair for ASPLOS 2021. He received the SIGPLAN Distinguished Service Award in 2024.
His honors include a Microsoft Research Fellowship, an NSF CAREER Award, a Lilly Teaching Fellowship, the Distinguished Artifact Award for PLDI 2014, the Most Influential Paper Award at OOPSLA 2012, PLDI 2016, and ASPLOS 2019; best paper awards at FAST, OOPSLA, SOSP, and OSDI; five papers selected as CACM Research Highlights, a Google Research Award, and a Microsoft SEIF Award; he was named an ACM Fellow in 2019.