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Daily Python News Question, Tips and Tricks, Best Practices on Python Programming Language Find more reddit channels over at @r_channels
Continuous profiling or function level data, only one survives the budget cut
Finance won, the monitoring bill came down, and the custom instrumentation went with it. Then the thinned out service broke and we had nothing useful. Trying to spend what’s left in the right place rather than guessing again.
Right now it’s between always on profiling with Pyroscope, or something like Hud that picks up function level detail without us writing spans per path. Either way it’s another argument about data leaving prod.
What did you keep when you had to cut?
https://redd.it/1xu7ufv
@pythondaily
How much does reviewer confidence matter in ICLR decisions? D
My ICLR submission received ratings of 3 / 3 / 2 with confidence 1 / 3 / 3. One weak accept comes from a low-confidence reviewer (Though their review seemed knowledgeable and pinpointed a key issue in the paper.) the other weak accept and the weak reject both have confidence 3.
How do ACs typically weigh a split like this after the rebuttal and discussion? Does the low-confidence positive review count for less, or is the average rating still a useful signal? And how much comes down to the substance of the reviews and whether the rebuttal resolved the concerns?
I know there is no fixed formula, but I'd appreciate hearing from authors, reviewers, or ACs who have seen similar score and confidence combinations.
https://redd.it/1xu3n5f
@pythondaily
I built an explorer of 2,100+ robotics papers covering 10 years of CoRL; with TLDRs, links to code, project pages, and research trends P
Heya roboticists and mind minders \~
I built a complete CoRL paper explorer, indexing 2,100+ robotics papers from the last 10 years (2017–2026). Added paper TLDRs, project pages, code, arXiv links, task chips, summary stats, research trends, and more!!
Trying to make robot learning research more accessible for the community - might build an open source autoresearch capability on top of it later
If you see something that's missing or want to add features/contribute, open a PR and i'll accept it :\]
paper explorer:
https://corl.operatic.ai/explore
repo on github - star to follow along!!
https://github.com/operatic-ai/corl-explorer
https://redd.it/1xtul3j
@pythondaily
How should authors handle a suspected auto-generated review at ICLR? Flag to the AC or just rebut? D
I'm curious how ACs and experienced authors think about this.
My situation: one ICLR reviewer gave a 2 with confidence 1. The other reviewers gave 3 with confidence 3. Pangram flags the review as 100% AI. I know detectors aren't proof on their own, but there are other signs too: all three listed weaknesses concern material deep in the appendix.
I'll respond to every point in the rebuttal regardless. My questions:
1. Is it worth sending a confidential comment to the AC, or does that tend to backfire?
2. For ACs: what evidence would you actually act on? Is a detector score meaningful to you, or only content-based issues such as generic or inaccurate criticisms?
3. How much weight does a confidence 1 review realistically carry in the meta review?
4. Has anyone flagged an AI generated review yet? What happened?
Thanks!
https://redd.it/1xtvipb
@pythondaily
Have people given up on ICLR reviews? D
Something weird I observed with my batch of papers and my own paper. Authors have not posted a single rebuttal yet, and no reviewer has responded to ours. This is a batch of almost 10-12 papers. Even papers with borderline fixable reviews have no responses from authors. Have people basically given up on ICLR?
https://redd.it/1xtt0px
@pythondaily
AI ‘slop’ is a social judgement R
https://www.nature.com/articles/s41562-026-02639-6
A new paper argues that AI Slop isn't something you can find in the content. It’s a social judgement, made when a community decides that some AI-generated content is unwanted. If we study 'slop' that way, we can actually measure it and govern it.
https://redd.it/1xtour7
@pythondaily
Every lab assignment in my course ended the same way: Export your notebook as PDF and upload it. Not self promotion this my first project i made D
​
And every time:
\- File → Download as → PDF → "xelatex not found"
\- Installed LaTeX (4 GB!) → "pandoc not found"
\- Fixed that → long code lines got cut off at the edge of the page
\- Tried browser print → charts split across pages
\- 11:58 PM, deadline at midnight, panicking 🙃
The online converters I found wanted me to upload my assignment to their server, were full of ads, or gave an ugly PDF that looked nothing like my notebook.
So I built my own: https://ipynbtopdfscanner.com
What it does:
\- Open your .ipynb → download a clean PDF. No LaTeX, no pandoc, no install
\- Runs in your browser, so your file is never uploaded
\- Long code wraps instead of getting cut off
\- Keeps charts, tables, math equations and outputs
\- Hide code, add your name on the title page, or keep the Jupyter look
\- Works on your phone too
\- Works for Colab, VS Code, Kaggle and Deepnote notebooks (download the .ipynb first)
Free, no sign-up.
Full disclosure: I used AI to help me build the site and write this post. The problem and the frustration were 100% mine though.
This is my first real project, so honest feedback would really help. If a notebook looks wrong in the PDF, tell me and I'll fix it.
https://redd.it/1xtnjhw
@pythondaily
Changing User Model
Hello everyone,
I am slightly new to the Django.
I am developing an app for a clinic, first they said it does not have to be with account and JWT's and all that but now they changed their mind.
It has been a while so a lot of migrations i made.
I know that changing the User model after a while is not healthy, i asked this to AI, it said that i can use another "app" in Django for user and i can do migrations etc. easily at there.
Is this the best approach for this?
What do you guys suggest?
Since i changed the User Model, migration is pretty problematic and i do not want to lose these datas.
https://redd.it/1xti1mg
@pythondaily
I need some guidance after a six month hiatus R
Short Backstory:
I'm a PhD student on my 5th year. I had a major mental breakdown on June 2026 after my second rejection on an IEEE Journal. After a lot of back and forth, I decided to not give up my PhD (for the time being).
Current Situation:
My current objective is to author my 3rd publication (IEEE Journal / Elsevier / etc level) in order to unlock thesis/dissertation-writing stage (as per my advisor's words). I'm working on the MetaboNet dataset, a standardized diabetes research dataset for continuous glucose monitor (CGM) and forecasting (https://metabo-net.org/). I've read their paper and their benchmark. I've pinned TCN (Temporal Convolutional Network) as my safest option to handle this.
I'd be ever so grateful if anyone familiar with the latest research trends on TCNs (or forecasting in general) could point me to what to look up. The ultimate goal of cource is the research novelty. Truth is that I tried to research it myself but anxiety and feelings of overwhelmingness got the better of me, can't tell if something would be considered novel enough for publishing.
Thanks for taking the time to read my post, wish you luck on all your ML/DL endeavors.
https://redd.it/1xpb6kb
@pythondaily
Wiimote in Python
Im having a ton of trouble trying to get a wiimote's data/Inputs. I've tried wiiuse and cwiid didnt seem to work. Idk if it has smt to do with me being a mac.
https://redd.it/1xspb09
@pythondaily
Pydantic in 100 lines
The title is obviously tongue in cheek, but in 100 lines we can get a quite a lot done to type-check dataclasses.
https://thibaud.space/2026/10/pydantic-in-100-lines-part-1.html
https://redd.it/1xtcexq
@pythondaily
Why are Anthropic's top researchers cited twice as much as OpenAI's? [D]
https://redd.it/1xt5xld
@pythondaily
Are AI-written reviews at ICLR 2027 marking down interpretability and marking up riskier capability work? d
Just a thing I noticed. Looking at the image from this thread it seems interpretability received the lowest scores. And looking at the ranking the highest rated paper (444 with highest confidence) is a paper proposing to let the model modify its context. Seems to confirm OpenAI's latest findings. Maybe it's just a coincidence.
https://redd.it/1xpob55
@pythondaily
Why is it always ICLR? Last year’s incident, this year’s rating drama D
ICLR 2026 had the OpenReview identity leak, followed by review scores being rolled back to their pre-rebuttal state. That was already a mess for authors caught in the middle.
Now, for ICLR 2027, ratings have been squeezed into a 1–4 scale. From what I’m seeing, most seem to be landing at 2.
How useful is this compression if papers with very different strengths and weaknesses end up in the same “weak reject” bucket? A paper needing a few convincing clarifications and one with substantial unresolved problems can receive exactly the same score.
This makes meaningful rebuttal engagement even more important. If authors address the concerns, reviewers should reassess their recommendation and explain any remaining objections. ACs need to evaluate whether the reasons actually justify rejection, rather than simply following the numbers.
Authors spend months or years on these papers. Wanting a consistent review process, substantive discussion, and reasoned decisions should be a basic expectation.
On top of that reviewers don’t turn up, and ACs go by the ratings. It’s like a lottery, your paper’s fate depends on which three or four reviewers it gets assigned to.
Anyone else finding this year’s scale frustrating?
https://redd.it/1xpxc38
@pythondaily
I built PrezScan: hardware-free face recognition college attendance in Flask + WebGL (<200ms) with campus Wi-Fi & GPS lock
https://redd.it/1xswn24
@pythondaily
Typed WebSocket APIs on Django Channels: routing, validation, DRF permissions, AsyncAPI docs and generated clients
Hi, I maintain chanx, a library on top of Django Channels. It gives a WebSocket API what DRF gives a REST API. You write one typed handler per message, and it takes care of the rest:
https://preview.redd.it/2a4zk22ax41i1.png?width=1480&format=png&auto=webp&s=2754b42c047d05845ae9bff20ad42cf7e541971c
Routing by message type. The `action` field picks the handler, so no more `if`/`elif` chain in `receive_json`.
Validation. Every message is a Pydantic model. A bad payload never reaches your handler, and the sender gets a structured error that says which field failed.
DRF permissions. Connections are guarded by the permission classes you already have, object permissions included.
Push from anywhere. broadcast_event_sync() from a view, a signal or a Celery task, through the channel layer.
Topics. Many streams over one socket (`discussion:42`, `notifications:me`), each with its own handlers and authorization.
AsyncAPI 3.0 docs. A spec and an interactive docs page, built from your handlers, so they can't drift from the code.
Generated clients. A typed TypeScript client (React, Vue, Svelte, Solid) or Python client from that spec. Rename a field in Django and the frontend build fails, instead of production.
Testing. Communicators that understand your message types, and capture_broadcast_events to check broadcasts without a socket.
Type-checked. Strict mypy and pyright, so a wrong message type shows up in your editor.
Other languages. The protocol isn't Python-only anymore. For TypeScript, chanx-js covers both ends: a server for Node (Express, Fastify, Hono, NestJS, Bun, Deno) and the client. For Go, chanx-go has a server and a client. They share the AsyncAPI format and the Redis channel layer with Django, so a Django view can broadcast to connections a Node or Go service holds. If one part of your app ever needs to move, it can move without the clients changing.
Django quick start: https://python.chanx.dev/en/latest/quick-start.html?framework=django
GitHub: [https://github.com/chanx-dev/chanx](https://github.com/chanx-dev/chanx)
TypeScript: https://js.chanx.dev/ · Go: https://go.chanx.dev/
If you already work with plain Django Channels and feel the pain of testing consumers and keeping track of every message shape, give it a try. I built it around exactly those inconveniences, from my own time with Channels. If it saves you some time, a star on GitHub or a mention to your team or friends would encourage me a lot to keep improving it. The library is actively maintained and developed, so issues and PRs are very welcome.
https://redd.it/1xu6mpp
@pythondaily
Missed Presentation D
Hello everyone,
For some reason if you miss paper workshop presentation then what happens?
https://redd.it/1xu1szu
@pythondaily
Thursday Daily Thread: Python Careers, Courses, and Furthering Education!
# Weekly Thread: Professional Use, Jobs, and Education 🏢
Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment.
---
## How it Works:
1. Career Talk: Discuss using Python in your job, or the job market for Python roles.
2. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources.
3. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally.
---
## Guidelines:
- This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar.
- Keep discussions relevant to Python in the professional and educational context.
---
## Example Topics:
1. Career Paths: What kinds of roles are out there for Python developers?
2. Certifications: Are Python certifications worth it?
3. Course Recommendations: Any good advanced Python courses to recommend?
4. Workplace Tools: What Python libraries are indispensable in your professional work?
5. Interview Tips: What types of Python questions are commonly asked in interviews?
---
Let's help each other grow in our careers and education. Happy discussing! 🌟
https://redd.it/1xtxsyq
@pythondaily
Wagtail Space 2026 is just ONE week away!
https://redd.it/1xtuqgg
@pythondaily
I built a Linux sandbox in Python, and then tried to break out of it
I use coding agents a lot and wanted to know what stops one from reading my credentials. So I built a sandbox in Python with no dependencies, then tried to break out of each layer.
Post: https://muhammadraza.me/2026/building-agent-sandbox-python/
https://redd.it/1xtr15j
@pythondaily
Is Python a good choice for learning algorithms and data structures ?
Is python a good choice for learning algorithms and data structures?
It has so many built-in methods and libraries that make things easy. Doesn't that make it harder to understand how things actually work under the hood?
Would C or C++ be a better choice for this purpose?
https://redd.it/1xto03d
@pythondaily
Gatekeeping rules for ICML2027? D
Any thoughts on how icml will be managed this year? With ICLR having 20 submissions limit per author, I find it hard to submit my works cause the research group of my supervisor is large and I am far from being in top students.
I had a NeurIPS submission which was kinda borderline (435), but got rejected. Now I resubmitted the improved version to ICLR and thought it would get good results, but it is now much worse😂😭 Having bad reviews on ICLR make me feel like I wasted 1/20 submissions
It feels like my new works are stuck due to the need of resubmitting the old ones. But the reviews sometimes are so random. Do you think ICML will have similar rules this year? Any suggestions where to submit apart from ICLR/ICML/NeurIPS?
https://redd.it/1xrp483
@pythondaily
Two AI architecture experiments I've been working on — PHSMLP and Pilot [R]
https://redd.it/1xt4amh
@pythondaily
Can we talk about the ML research coming from China? D
In this post I am not trying to bash Chinese researchers by any means but I have gotten VERY curious in the past few years as to how research is conducted in China.
It is no secret to anyone with a pair of eyes that Chinese researchers in ML (especially Chinese researcher based in China) are extremely productive to the point of defying common sense. We are talking about 100-200 papers while an undergraduate/graduate student. 1 paper a week is not unheard of. Can easily pull up Google scholar profiles of random Chinese researcher or PhD student with hundreds of papers submitted to dozens of different conferences. At the same time they have co-authors in the range of dozens and sometimes hundreds, especially when it comes to benchmarks or surveys.
The research seem to concentrate heavily in the LLM/Multi-Modal space, with very few novel mathematical derivation/arguments but tons of experiments, sometimes ranging almost hundred page of experiments. I ran some of these work in ChatGPT/Claude to check for possible LLM generated artifacts and usually it quickly returns a list of suspicious sections, but the paper were subsequently accepted in ICLR/AAAI and similar conferences anyways.
I am confused as to what exactly is happening. I understand that Chinese folks have entire publishing ecosystem and hiring ecosystem that is insular to the west. It is well known that the Chinese faculties working in the states would routinely advertise for academic positions in places like Xiaohongshu (RedNote) or Douyin or BiliBili, which creates a certain level of unfairness. But I wonder if similar network exist for people who want to publish papers to get together to "scratch each other's back", or give someone the proper credentials (some kind of academic nepotism).
Would appreciate it if someone can give us an inside look into how this all work.
https://redd.it/1xteym8
@pythondaily
Seeking Research Collaborators and Funding: Investigating Biological Intelligence and Neural Computing D
​
Can living neural systems provide new insights into computation, adaptation, and intelligence?
We are developing an independent research initiative called Transference, through Paraibis Global Trade Solutions, to investigate the intersection of biological computing, neuroscience, artificial intelligence, and emerging computational architectures.
Our research has progressed from theoretical investigation into computational analysis of experimental neural activity datasets.
Research completed
We gratefully acknowledge FinalSpark, which provided access to the FS369 and FS437 experimental datasets used in our research.
Using these datasets, we conducted a structured discovery and independent validation programme investigating neural responses to electrical stimulation.
Our original hypothesis did not fully replicate in the independent validation dataset. However, the analysis revealed an interesting pattern involving changes in electrode-level activity and temporary compensation among different recording channels.
This observation has informed a new hypothesis concerning minority-channel compensation and transient recruitment.
Importantly, this new hypothesis was generated from our subsequent analysis and has not yet been independently validated.
We consider this distinction essential to maintaining scientific integrity.
What we hope to investigate next
We are seeking opportunities to conduct prospective experiments involving living neural systems.
Our principal interests include:
\- Neural responses to controlled electrical stimulation.
\- Electrode-level activity redistribution and network dynamics.
\- Computational modelling of biological neural behaviour.
\- Machine learning methods for analysing electrophysiological activity.
\- Reproducible experimental methods and independent hypothesis testing.
\- The longer-term computational potential of biological intelligence.
Seeking research collaboration and financing
We are interested in connecting with researchers, laboratories, universities, research foundations, funding organizations, and individuals working in related fields.
We would particularly welcome discussions involving:
1. Access to experimental biological computing infrastructure.
2. Collaborative neuroscience and electrophysiology research.
3. Independent experimental validation.
4. Research grants, sponsorship, or other legitimate financing mechanisms.
5. Technical criticism and suggestions for improving our experimental approach.
Our objective is not to claim that we have demonstrated a new computing technology. Rather, we want to establish the experimental evidence necessary to determine whether the phenomena we are investigating have broader scientific or computational significance.
Acknowledgement
We appreciate FinalSpark for providing the experimental datasets that made this stage of our independent research possible.
Their work in biological computing can be explored at:
https://finalspark.com
Invitation to the community
We would be interested to hear from researchers and others familiar with biological neural networks, multi-electrode arrays, neural stimulation, electrophysiology, or biological computing.
What experimental controls, validation methods, or computational approaches would you consider essential when investigating changes in neural activity following stimulation?
We also welcome private discussions with potential scientific collaborators and research sponsors.
Research programme: Transference — Paraibis Global Trade Solutions
Contact: info@paraibis.com
Thank you for any constructive feedback or opportunities for collaboration.
https://redd.it/1xtct76
@pythondaily
Would you pick your strongest model to coordinate other agents? Results from WorkflowArena R
Suppose you’re putting together a team of agents. Picking the strongest model as the coordinator seems reasonable. We wanted to test whether its workflows would actually help other models do the work.
In one experiment, we gave Opus 5.5 and GPT-6 Astra the same 32 challenging tasks and asked them to write workflows for four execution models. They could divide the work and instruct workers, but couldn’t edit the workspace themselves.
Opus’s static workflows improved all four executors, adding 6.41 points on average to their continuous scores compared with letting them orchestrate themselves. Astra’s static workflows lowered the average by 2.53 points, despite Astra being a stronger system than all four executors in the main benchmark.
So choosing a coordinator based on its own task performance can miss something consequential: whether the way it organizes work is useful to the models carrying it out.
This is part of WorkflowArena, our team’s new benchmark at Einsia. It covers 145 long-horizon tasks across eight domains. Agents choose their own division of work; we record the delegation and check whether the final deliverable meets the task’s requirements.
For anyone building mixed-model teams, I’d be interested in how you evaluate that choice today. Do you test the coordinator separately, or mostly judge the whole setup by its final output?
Project and workflow replays: https://lab.einsia.ai/workflowarena/home.html
Paper: https://arxiv.org/abs/2611.12856
Code: https://github.com/Einsia/WorkflowArena
https://redd.it/1xt9hbe
@pythondaily
CASP-DREAM TAP Challenge: TCR + peptide-MHC activation prediction with unseen test peptides N
A new contest for anyone training sequence models on TCRs. Given a TCR sequence and a peptide-HLA, predict whether the T cell activates. Inputs are amino acid sequences, the benchmark is about 5,800 labeled combinations, and every test peptide is at least four edits (Levenshtein distance) away from any peptide with a publicly reported TCR specificity. Scored by AUROC.
Synapse lists training data options, including the public VDJdb database and Harell Data's proprietary TCR datasets provided by Adaptive Biotechnologies.
Registration is open now: https://www.synapse.org/Synapse:syn77965030/wiki/643867
https://preview.redd.it/bubc341dw10i1.png?width=2160&format=png&auto=webp&s=22654b3a80b87325d4b1bb6762d905463fe4d1c8
https://redd.it/1xpn4gp
@pythondaily
Tim Peters has passed away
Tim died July 15, at his home in Ashland, Wisconsin. He was 74. We don't know any specifics yet.
In case you don't know who Tim Peters is, he was an early pioneer in helping shape the Python language and its runtime. He invented a new sorting algorithm ("Timsort"); a descendent of it ("PowerSort") is still used in Python today, as well as in Java, and several Javascript runtimes (Chrome, Node). He dashed off The Zen Of Python in response to a Usenet post asking about best practices. So many things in Python are directly due to Tim. Online, he was funny, charming, helpful, and had a depth of knowledge and wisdom that was frankly unmatched. He'll be missed.
https://www.bratleyfamilyfuneralhomes.com/obituary/timothy-peters
https://redd.it/1xt65ah
@pythondaily
I built a music streaming app for Android that runs its own Python server inside the APK
Hi everyone.
I've been building a personal project called BeatStream: a music streaming app for Android. Sharing it to get feedback.
The part I think is interesting: instead of a thin client talking to a remote server, the whole backend runs INSIDE the APK. It bundles a Python + Flask server using Chaquopy, and the WebView talks to it on 127.0.0.1. So the app is self-contained, no external server needed.
For audio I went native: playback is handled by ExoPlayer + MediaSession, so it keeps playing with the screen off or when you switch apps, with media controls in the notification (play/pause/next/prev and a seek bar). The web UI acts as the remote and stays in sync with the native player.
Features:
\- Search and play
\- Background playback with notification controls
\- Auto-generated mixes (endless queue)
\- Karaoke with synced lyrics when available
\- Repeat, likes, playlists
\- Spanish UI
It's a work in progress, so it's not perfect. The karaoke sync isn't always spot on, and background playback can still get killed on phones with very aggressive battery savers. Honest heads-up.
Stack: Python/Flask, Chaquopy, ExoPlayer/media3, vanilla JS single-file SPA.
Repo + APK: https://github.com/dereeqw/BeatStream/releases/tag/v1.0.0
Would love feedback on the architecture or the UX. Thanks for reading.
https://redd.it/1xt3va8
@pythondaily
3321 ICLR, should I rebuttal? R
Looks like this killer just doesn’t understand my paper. He said my abstract is not well organized, hard to follow...my definitions are also hard to follow..., missing important explaination... etc.
but the other three reviewers could read it. they all understood my idea. Why couldn’t he???
https://redd.it/1xqrv2a
@pythondaily
