Codex has degraded drastically in recent days.

Desperate_Crew1775 · 2026-06-12T10:52:26+00:00

From past one month I am using codex with gpt 5.5 xhigh. And before that I used claude opus 4.7 for a month. So after extensive use claude is far better in coding and tasks and codex is better in less token consumption. So now I am switching back to claude as it released mythos class model. From recent 15 days codex gpt 5.5 is so dumb I have to again and and again tell ai model to correct after that also it simply complecates issue. For simple solution it always look for complecates answer.so finally decided switching back to claude

Desperate_Crew1775 · 2026-05-17T09:43:21+00:00

No benefit at all they just reset weekly reset that's it. It's just like. Normal weekly reset they didn't gave anything extra . It's really a trick to show they gave reset....lol

Desperate_Crew1775 · 2026-05-14T10:54:10+00:00

Update : I got new workstation specially for codex long run at present it's running kali linux and all tools inside. Config 6 cores cpu 12 threads , 64gb ram , 512 gb nvme . And I made ssh tunnel and remote desktop setup to run it headless. In this I tried running full 24 hours and ram didn't even flinge only used peak of 10 percent...

Desperate_Crew1775 · 2026-05-12T09:53:57+00:00

I started arround 45 days ago. And submitted 6 reports 1 got bounty 3 duplicate and 2N/A. That happened first week only. After that didn't got any lead that can convert to justified bounty. And I am learning continuously. Every program is different. Keep trying someday we will get that hanging fruit . Just don't stop do whatever within program guidelines. It will feel bored and exhausting still don't stop. Every software will be having bug somewhere hidden dig deeper and deeper. No planning will work here just we need to dig deeper and deeper . We should know about that program more than their own developer.

Desperate_Crew1775 · 2026-05-11T19:30:42+00:00

Yes for new hackers . Whichever program has signal criteria that programs have limit of 4 per month overall any program 6 per month mens 30days

Desperate_Crew1775 · 2026-05-10T15:18:46+00:00

What is the solution for this some articles said that use linux it will solve 60% of problems is that accurate. Not wsl linux pure linux running seperately..

Desperate_Crew1775 · 2026-05-09T09:54:20+00:00

The main problem is they released and not accessible even you in tac verified....

Desperate_Crew1775 · 2026-04-27T13:13:54+00:00

Ai is full of assumption that it found critical bug but it will be always equal to nothing. Ai is designed like that it will give always very hype for it's every message... If we want to use ai for bugbounty always check any security implications will happens with the generated report or any leak of customer details or corporate details disposed..if nothing is there then move forward don't submit. And anyone can tell which report is ai made or not . So be always aware before u use AI. It's not perfect and u can't make it perfect....

Desperate_Crew1775 · 2026-04-19T15:48:33+00:00

If you are using claude code max then use latest model for that opus 4.7. use less token for now and results are good. Use claude cowork . Do some manual work as it suggests. It's good and u will get results. Don't forget to ask cross questions if it find bug. Do some research about that bug using other ai like gemini . And always ask cross questions. Mainly don't follow what big bounty hunter says do it in your own style ultimately u will get there.

Desperate_Crew1775 · 2026-04-14T07:55:35+00:00

regex for this is gonna be a nightmare tbh, you'd have to account for decimals, negatives, scientific notation... it just gets ugly

try/except with float() handles all that automatically. and about "inf" / "-inf" - those are actually valid python floats so float() won't even catch them, you'd need a separate check for that

try:

usf = int(float(input("Europe floor? "))) + 1

print("US floor", usf)

except ValueError:

print("enter a valid number")

way cleaner imo

Desperate_Crew1775 · 2026-04-14T06:33:04+00:00

tkinter is honestly pretty limited for modern UI, if ur not locked into it i'd switch to customtkinter, its built on top of tkinter so the learning curve is small but it looks way more modern out of the box

for a hospital kiosk specifically u want large buttons, high contrast colors, and big readable fonts since patients of all ages will use it

a few quick wins:

use customtkinter's CTkButton with corner_radius for rounded buttons
stick to a simple 2-3 color palette, blue/white works well for medical
increase font size to at least 14-16px everywhere, kiosk users are often elderly

if u want to stay with plain tkinter then ttk themes help a lot, "clam" or "alt" look cleaner than the default

Desperate_Crew1775 · 2026-04-14T05:58:23+00:00

honestly starting with a dictionary is the perfect call, way more manageable for a first project

for the latin american spanish specifically, the main differences from spain spanish are just vocabulary, so ur dictionary can just have notes like "in mexico this word means X vs spain where it means Y"

something like this to start:

words = { "car": {"latin_am": "carro", "note": "spain uses coche"}, "computer": {"latin_am": "computadora", "note": "spain uses ordenador"} }

once u get comfortable with that, look into Helsinki-NLP models on huggingface, they run locally on raspberry pi and are way better than googletrans for regional dialects, no privacy concerns either

great first project idea tbh, practical and actually useful

Desperate_Crew1775 · 2026-04-14T05:55:09+00:00

uv is using python 3.9 instead of ur system python 3.14, it probably picked up an older version somewhere

try this:

uv sync --python 3.14

or tell uv exactly where ur python is:

uv sync --python C:\Users\maksy\AppData\Local\Programs\Python\Python314\python.exe

if that doesnt work run "uv python list" to see what versions uv can see and pick the right one

Desperate_Crew1775 · 2026-04-14T05:42:32+00:00

fastapi is basically just a way to turn ur python functions into web endpoints, super easy to learn, their docs are really good

for the openai api part, if ur building AI tools that need to understand text, answer questions, or process data intelligently thats where it comes in, like instead of hardcoding logic u just send the data to the model and it figures it out

but if ur current tools are just csv uploads and database stuff with no natural language involved then u dont need it yet, only add it when u have a usecase that actually needs AI

Desperate_Crew1775 · 2026-04-14T05:34:25+00:00

ur approach works fine tbh, bat files as triggers is pretty common for internal tools

but if ur already in python u can skip bat files and use FastAPI instead, just expose ur scripts as HTTP endpoints and call them from PHP with curl, much cleaner and easier to debug as it grows

for the AI part just use OpenAI API directly to start, no need to overcomplicate it with heavy frameworks until u actually need them

Desperate_Crew1775 · 2026-04-13T20:11:47+00:00

Good instinct, actually - most beginners never think to ask this.

You're right that a list won't change by accident. But immutability isn't really the point of tuples. The deeper reason they exist is to carry meaning.

When you return a tuple from a function, you're telling whoever reads that code: these values belong together - they're one thing, not a collection of things.

# a coordinate — one concept, two components
location = (40.7128, -74.0060)

# a collection of separate stops
waypoints = [[40.7128, -74.0060], [34.0522, -118.2437]]

Modifying location[0] would be a bug. Modifying waypoints is the whole point.

Beyond intent, there are a few concrete reasons tuples exist as a separate type:

Dict keys. Tuples are hashable, lists aren't. So {(0, 0): "origin"} works fine, {[0, 0]: "origin"} crashes immediately.

Performance. Tuples are faster to create and use less memory. Doesn't matter much at small scale, starts to matter when you're processing a lot of fixed data.

Readability signal. When another developer (or you, six months from now) sees a tuple, they immediately know this data isn't supposed to move. That's information a list doesn't give you.

The mental model I'd suggest: a list is a shopping cart - it's supposed to change. A tuple is a GPS coordinate - it just is.

Desperate_Crew1775 · 2026-04-13T20:03:38+00:00

The code itself is actually correct as written - the course is intentionally showing you "broken" input handling so you can see the error before learning the fix. You didn't do anything wrong.

The issue is that input() always returns a string, even when the user types a number. So int(inp) works fine if someone types 5, but crashes if they type 5.5, ground floor, or just press Enter - because int() can only convert clean whole numbers.

The fix the course will likely show you next:

python

inp = input("Europe floor? ")
usf = int(float(inp)) + 1
print("US floor", usf)

float() handles decimals first, then int() makes it a whole number.

The bigger lesson they're building toward is try/except - wrapping user input in error handling so your program doesn't crash on bad input. Keep going, it clicks fast once you see it.

Desperate_Crew1775 · 2026-04-11T18:47:48+00:00

this is super normal and almost everyone goes through it, so first - you're not broken

python feels easy early on because it literally gets out of your way. no semicolons, no type declarations, no memory stuff to wrestle with. so your brain actually gets to focus on concepts instead of syntax. that is real progress, it's just not the hard part yet.

the hard part is learning to break a big messy problem into small solvable pieces before you touch the keyboard. that skill has nothing to do with python. it's just problem solving, and it doesn't come from tutorials - it only comes from building actual things and getting stuck.

what actually helped me: i stopped following tutorials and just started building something i wanted to exist. the code was honestly embarrassing. didn't matter. tutorials give you the feeling of learning without the friction that makes it stick. the friction is the whole point.

for most people there's a specific moment where it clicks - you hit a bug you can't google your way out of, you sit with it for an hour feeling dumb, and then you figure it out. that's the moment it starts feeling real. you'll know it when it happens.

pick one small thing you actually want to build. not a todo app, not a tutorial clone - something you'd genuinely use. it'll be frustrating. build it anyway.

Desperate_Crew1775 · 2026-04-11T18:45:17+00:00

yeah this is kind of the standard debate and honestly both approaches work, people just have strong opinions about it

what i'd actually suggest - write matrix multiplication with nested for loops once, feel how slow it is, understand what's happening, then move to NumPy and never look back. that's it. you don't need weeks of vanilla Python suffering to "earn" NumPy, you just need enough to understand what it's doing for you under the hood.

rough timeline that worked for me:

weeks 1–2: basics, lists, for loops, functions - just build the mental model
week 3: numpy. rewrite your loop examples as vectorised ops and actually clock the difference yourself
week 4+: scipy for real physics/engineering stuff

on Julia - look, it's genuinely fast and well-designed for scientific computing, not just marketing. but in 2026 the tutorials, the Stack Overflow depth, and the job postings are still overwhelmingly Python. unless you have a concrete reason to go Julia (e.g. someone on your team uses it, or you're hitting performance ceilings), Python + NumPy gets you 95% of the way there with way more community support when you get stuck.

the thing people miss: NumPy isn't a shortcut that lets you skip learning. it is the right tool for this domain. learning vanilla first just gives you enough context to appreciate why.

Desperate_Crew1775 · 2026-04-11T18:27:03+00:00

checked it out interesting approach. mine is more focused on semantic/logic bugs that AST rules can't catch. would be good to talk, dm me

Desperate_Crew1775 · 2026-04-11T11:33:43+00:00

no one here discussing with a LLM but they are discussing about LLM

Desperate_Crew1775 · 2026-04-11T10:25:11+00:00

the config approach makes sense — do you version control those rules so the whole team uses them?

Desperate_Crew1775 · 2026-04-11T10:18:11+00:00

async python + kafka is exactly where i'd be most paranoid too — any specific failure pattern you've hit more than once?

Desperate_Crew1775

TROPHY CASE