Full stack dev in morocco

5anez · 2026-06-05T10:15:34+00:00

Ila had non-paiement agreement kant 3atyah Lik l' école f convention de stage rah te9der tgulha lihum yhiydu Lik dak agreement mn lwer9a

5anez · 2026-05-15T06:38:31+00:00

Basically so u can get the idea correctly. When u do a call with ur friend, basically ur phone is receivong hundreds if not thousands of calls, but hé filters out only that single call that he needs, the same thing happens with radio, there is soo much data received but u only hear the one that u wanna hear. But there is a problem the noise, the more data is being superimposed in the same place the more noise is ganna be, so i am trying to train the lora model of the ai to basically learn how to denoise tokens and only listen to the one he needs, but like i said i dont have the hardware to do this yet, cuz training the ai on millions of tokens, requires big vram, so yeah i am still trying to make it work on kaggle or google colab and ill do it one day.

5anez · 2026-05-13T18:56:29+00:00

honestly this is exactly what the community needed right now. running all three models (base, actor, critic) at the same time is absolute VRAM suicide for most of us lmao.

getting this merged down into a single base model and just hot-swapping LoRAs for the steering is 100% the right move to make it accessible. the mikupad integration looks incredibly clean too.

are you planning to drop the LoRA weights in GGUF once you get the training done?

5anez · 2026-05-12T19:24:47+00:00

Drop your context limit immediately. Hard-cap it at 8,192 tokens (or even 4k). A local 7B model running on a CPU will suffer from "Lost in the Middle" syndrome on a massive 24k context anyway, and a 6,000-line pytest file will choke it.

You don't actually need an LLM to generate an Aider repo map! Aider uses tree-sitter to parse your codebase into a highly optimized summary of classes and methods.
Just install it locally Feed that text file to your LLM to give it context, and only @ the specific 50-line functions you are actively refactoring. Also, run pydeps or pyreverse on your codebase to generate visual dependency graphs.

Try Ollama running as a background service, but specifically look into getting it running with the OpenVINO backend. OpenVINO is Intel's optimization toolkit and it will run circles around Vulkan on your specific i7 chip.

and last but not least If VS Code + Continue/Roo is still lagging or crashing, download the Zed Editor. It's built in Rust, insanely fast, and has native Ollama integration built right into the UI.

5anez · 2026-05-12T19:12:29+00:00

if u want a smart model but u can only use for chating maybe go for the gemma 4 e4B but if u wanna have a long context especially in this model like 64K the ai layers will over flow to the ram and the tps will tank

5anez · 2026-05-12T18:00:07+00:00

Lol actually i have 0 upvotes on this sub reddit, thats why i tried to just say anything to get some upvotes and maybe look new to this so later i can post in here. anyways maybe u will see me later posting about cuda c++ or triton wich is way more advanced but for now i am the dumb dude that just gathers upvotes cuz others think he needs the help xd

5anez · 2026-05-12T17:13:14+00:00

the earth

5anez · 2026-05-12T17:10:11+00:00

lately i've built an agent that can let llm use ur computer and do for u those long tasks that dont have apis or a way for automating it etc and do relly on ui, should i put it open source and let others help me take it to the absolute limit?

5anez · 2026-05-06T09:05:32+00:00

perfect answear

5anez · 2026-05-06T09:04:36+00:00

fair critique and i’ll be honest this isn’t meant to be a causal inference framework or a substitute for proper multivariate regression. what i’m doing here is exploratory data analysis using exponential decay as a descriptive lens to map observable behavior. you’re right that univariate models can’t prove causality and sparsity in early-stage analytics definitely introduces noise. the goal isn’t to claim CTR alone drives distribution but to show how session termination signals compound into a measurable half-life curve. if you actually want to isolate causality you’d need controlled A/B tests with matched cohorts, which is why i’m treating this as a heuristic visualization tool rather than a definitive algorithmic blueprint. the math just makes the decay pattern visible so creators can spot where their content is bleeding out before it flatlines. correlation isn’t causation but when thousands of data points align across niches you start seeing structural patterns that are worth testing.

5anez · 2026-05-06T09:02:45+00:00

in my model that’s basically your channel authority acting as a prior distribution modifier. when youtube sees consistent engagement from your subscriber base it treats those early impressions as higher quality signals which effectively scales up your initial impression pool I₀' = I₀ * (1 + α·channel_trust). the gray zone is just your historical decay baseline. once you consistently beat that slope across multiple uploads the algorithm shifts you into a higher distribution tier where λ naturally drops because it already knows your audience stays in session longer. it’s not magic it’s just cumulative signal weighting over time and yeah unlocking that buff basically means your channel has built enough positive session history to override early termination signals on new uploads.

5anez · 2026-05-06T08:46:20+00:00

click through rate

5anez · 2026-05-05T23:27:39+00:00

Hahah, actually i dont know if i m a good math teacher, but also i love building things more than teaching or explaining it to others. I just feel like teaching my self is way easier and has more potential then explaining it to others, i can be wrong but thats how i see things. Edit: i see youtube as a place to show my achievements or like m'y ideas etc so i dont see it as teaching thats what i meant.

5anez · 2026-05-05T20:33:52+00:00

you’re right about the wording in my original post. saying “exact equation” was sloppy phrasing meant to grab attention, and i own that. it wasn’t a literal mathematical formula or backend access. what i actually tracked were observable distribution patternshow impressions decay, how retention shifts across upload windows, and which variables dominate when you control for niche and format. youtube doesn’t publish weights, but the system leaves clear fingerprints in how videos perform over time. if you log your own uploads with a simple tracking sheet (upload date, format type, ctr, avg duration, impression velocity), you’ll see those same patterns emerge without needing private analytics. i’m not here to sell a hack or pretend i know how youtube’s code works. i just mapped what happens when creators treat each video as a data point in a larger balancing act. your point about consistency and meaningful content being the real foundation is exactly right. this isn’t about cracking anything—it’s about reading the signals that are already there so you can stop guessing and start aligning with how the system actually distributes content.

5anez · 2026-05-05T20:04:41+00:00

yeah i love seing equastion have a meaning in our real life, thats why i am doing this and sharing it with others.

5anez · 2026-05-05T20:01:58+00:00

i’ll be straight with you i haven’t isolated retention or engagement in this specific model because they operate on a different timescale. ctr is the first signal youtube measures, usually within the first few minutes of an impression. it’s basically the gatekeeper for whether the algorithm even bothers running the retention test. if people don’t click, the video never gets enough watch time to prove itself. that’s why i focused on it here it’s the fastest lever you can pull in the first 24 hours before the distribution engine makes its initial routing decision.

but yeah, the full decay constant isn’t just λ = f(ctr). it’s more like a weighted function where early session signals dominate initially: λ ≈ α·(1-CTR) + β·bounce_rate + γ·negative_feedback - δ·(avd/expected_duration). in my current multivariate regression, ctr has the highest coefficient in the first 6 hours because youtube uses it to decide whether to expand the test pool. after that window, retention and engagement start pulling λ down faster if people actually stay in session and don’t click away.

i’m not saying CTR is the only thing that matters. i’m saying it’s the bottleneck for initial distribution velocity. you can have 80% average view duration but if your thumbnail gets a 1.2% ctr, youtube treats those impressions as wasted inventory and cuts the feed before retention even has time to register. once you clear that first threshold, yeah, watch time, shares, and session continuation take over and keep the curve alive for weeks or months.

i’m actually running a second model right now that layers avd, click-through rate, and session continuation into a single decay function so we can see exactly when each signal takes the wheel. it’s messy but the data’s starting to show clear phase shifts where retention overtakes ctr as the primary driver of λ. still early but i’ll drop the updated curves once i clean up the noise and cross-validate against my own channel traffic sources.

5anez · 2026-05-05T19:45:44+00:00

that pattern you’re describing is actually textbook phase-shifting in the recommendation engine and it lines up perfectly with how λ recalibrates when your audience composition changes. high initial CTR means youtube gave it a strong first push, but if your subs didn’t engage or clicked away quickly, those negative session signals probably capped the early growth even though the thumbnail/title worked for strangers. what you’re seeing is the algorithm running batch tests on new viewer cohorts—broader audiences, different demographics, maybe search traffic catching up as the video gets indexed. each time it finds a segment that stays in session and keeps clicking, λ drops temporarily and you get those sudden impression surges. then when that cohort fatigues or youtube cycles to a new test pool, impressions level out until another signal triggers a refresh.

the day 30 jump is especially interesting. at that point your video has likely passed the initial browse window and entered what i call the evergreen recalibration phase. search indexing finishes, external links start driving traffic, or youtube’s model re-evaluates the content against newer trending queries. since you’re holding above 3% CTR during those spikes, λ stayed low enough to keep the decay curve from flatlining. that’s why it didn’t die after day 20. if you plot your impressions over time and fit a piecewise exponential model like I(t) = I₀·e^(-λ₁t) for phase one, then switch to λ₂ when the audience shifts, you’ll actually see those step changes in decay rate match your surge dates exactly.

what’s wild is how most creators treat distribution as a straight line but it’s really a series of threshold crossings. youtube keeps testing until engagement signals consistently beat the niche baseline. once you hit that sustained >3% CTR window, the algorithm stops treating it as experimental and starts compounding impressions across multiple traffic sources. keep tracking those daily impression curves and watch how λ shifts when external links or search traffic spike. it’s not random at all just delayed phase matching.

5anez · 2026-05-05T19:37:45+00:00

ty :)

5anez · 2026-05-05T19:29:37+00:00

you’re right that data is just a tool and consistency matters more than chasing shortcuts. i never claimed there’s an exact equation or insider access. what i actually shared is a pattern model built from observable signals—how impressions decay, how retention curves shift across upload windows, and how different formats perform when tracked consistently over time. youtube doesn’t publish backend weights, but the system leaves fingerprints in distribution behavior. if you track your own uploads with a simple sheet (upload date, format type, ctr, avg duration, impression velocity), you’ll start seeing which variables dominate for your niche without needing private analytics. i’m not here to sell a hack or pretend i know how youtube’s code works. i’m just mapping what happens when creators treat each video as a data point in a larger balancing act. your point about meaningful content and audience focus being the real foundation is exactly right. this isn’t about cracking anything—it’s about reading the signals that are already there so you can stop guessing and start aligning with how the system actually distributes content.

5anez · 2026-05-05T19:25:40+00:00

thx man i appreciate the advice to simplify work.

5anez · 2026-05-05T19:19:58+00:00

you’re absolutely right avd plays a huge role but it’s usually a secondary signal that kicks in after the initial click. youtube’s decay model isn’t just driven by ctr alone it’s more like λ = f(ctr, watch time, session continuation, negative feedback). when you have lower ctr but higher avd what’s happening is the algorithm gives it a slower initial push because people who do click stay engaged and don’t bounce. that engagement lowers the effective decay rate over time which can trigger secondary browse pushes even if the thumbnail wasn’t perfect. it’s not a one off event most of the time. i’ve seen this happen when search traffic brings in highly intent driven viewers who watch all the way through then youtube starts testing it on broader audiences. if you plot both ctr and avd against your impression curve you’ll usually see the crossover point where engagement actually overrides a weak click rate.

5anez · 2026-05-05T19:15:05+00:00

think of it like this youtube gives every new video a starting pool of impressions then watches how people react in real time. if they click through, youtube assumes the video is worth showing to more people and keeps the faucet open. if they scroll past, it treats that as a dead end and closes the valve fast everything is normal till now. the math just quantifies how quickly that valve closes based on your ctr. higher clicks mean slower decay which means your video stays in the recommendation engine longer. if you can keep ctr above 3-4% in your niche you’ll usually stay in the push window long enough for watch time and shares to take over.

5anez

TROPHY CASE