u/Inevitable_Wear_9107

I spent too much time looking for a PPC lever when the account needed a broom. Tiny 3 SKU catalog, about 44k a month, and 11 search terms had each crossed 19 clicks with no orders. That should have been a Monday morning fix. Instead I let it sit because budgets and campaign structure felt more urgent. Could be wrong, but that was just avoidance dressed up as prioritizing.

The routine is now fixed. Every Monday I pull the last 30 days of search terms, placements, and business data. I review promotion ideas, negative ideas with spend logic, and listing copy suggestions based only on terms with at least 2 orders. I check every line manually. No live bid edits, no auto pushing, no account changes unless I understand the reason.

The one bad suggestion was a copy idea that tried competitor wording in A plus because shoppers were searching it. I rejected it. I am not putting a compliance headache into the listing to chase a few more clicks.

After 6 weeks, TACOS moved from 17.8 percent to 14.9 percent. I moved 7 buried converters into exact, cut obvious dead weight, and cleaned up title plus A plus copy so the listing matched buyer language again. Mostly boring hygiene, honestly. (MuleRun was only the file generator.)

I was watching the NVDA earnings call yesterday and Jensen Huang said something on CNBC after that I had to rewind twice. He straight up said Nvidia has "evacuated" and "really largely conceded" the China AI chip market to Huawei, and told investors to "expect nothing" from China. This is a company that had 95% of that market a few years ago and the CEO is now publicly saying it's at zero.

What makes it even stranger is the U.S. already approved about 10 companies (Alibaba, Tencent, ByteDance, JD, etc.) to each buy up to 75,000 H200 chips. Not one has shipped. Beijing blocked them at customs back in January and told everyone to buy domestic. And the numbers on the Huawei side are getting hard to ignore. Ascend chip revenue projected at $12 billion this year versus $7.5 billion last year. ByteDance alone put in $5.6 billion in Ascend orders, from basically nothing before.

So I started looking into how to actually get exposure to the China domestic chip supply chain and quickly realized KWEB and CQQQ basically don't do it. KWEB is all offshore internet, zero A shares. I ended up finding CNQQ which is about half A shares and half HK, and actually holds names like Cambricon and Zhongji Innolight alongside the usual Alibaba and Tencent. Still doing my homework on it, and obviously anything single country China comes with its own bag of headaches. But the fact that Jensen Huang himself said "conceded" on camera made me take the domestic chip story a lot more seriously than I had before.

i work in logistics and spent a year reading about niche affiliate sites before finally just making one. portable solar chargers because i own three (Anker 521, Goal Zero Nomad, BigBlue 28W) and figured i could at least write something honest.

Amazon rejected my affiliate application for no reason. reapplied saying the same thing, got approved. still don't get it. threw the actual page together on MuleRun in like two hours, which felt backwards considering the affiliate drama.

six weeks: 422 visitors, 34 clicks, 4 purchases, $9.56. Google didn't index it for a full week. crept to page 2 for "best portable solar charger camping" by week 4 and the BigBlue gets all the clicks for some reason. 4% outdoor commission is painful. putting up two more pages this month, probably headlamps.

Quick context: M3 max 64gb, currently running llama 3.3 70b q4 as my daily driver via ollama, qwen3 coder 30b for code (switched from qwen2.5 earlier this year), mlx for the smaller stuff. tried llama 4 scout earlier this year but 64gb is too tight at q4 once you leave headroom for context, sticking with 3.3 70b until i upgrade hardware. moved off the claude api around september last year. the cost wasnt the main reason, the data was. i do contract work with NDAs and i was getting uncomfortable about how much code and how many internal docs were going through anthropics servers.

Heres the thing i didnt expect. once i moved my LLM local, everything else upstream and downstream of the LLM started feeling wrong by comparison.

Example. i was on rewind.ai for a long time because the screen memory thing is genuinely useful for me. then meta bought them in december and the mac app went away, like everyone elses. tried a couple of the diy alternatives that picked up steam after, screenpipe in particular, didnt quite stick for me.

I was using granola for meeting notes. same story, transcripts go up. uninstalled.

I think a lot of you are running into this same thing if youre being honest. you spend 6 months building a private LLM stack, and then you realize half the productivity stuff orbiting your work is still shipping your raw artifacts up to a saas backend, full meeting transcripts, full doc bodies, sometimes whole screen frames, and you havent gone local on the data side at all. youve only gone local on inference. those are two different fights and most people havent had the second one yet. and even on the data side its a spectrum, not a switch, "raw never leaves" is a different bar than "no packets out at all", and you have to actually decide which one is your threshold before you can pick tools.

What i ended up with as of now:

ollama for inference, llama 3.3 70b q4 for general, qwen3 coder 30b for code, nomic-embed-text-v2 for vectors
local whisper.cpp for transcription, the cpu lift is fine for my use
obsidian, plain markdown, encrypted volume on disk
AirJelly for screen and cross app memory, the one item in this list that isnt strictly local, caveats below

i was most skeptical about that last one. to be upfront, airjelly isnt pure offline. captures themselves are stored on disk locally, but the vision model inference round-trips to their backend with just the cropped frame it needs to analyze. raw screen history doesnt leave the machine, which was my actual threshold. if your bar is "literally no packets out" this isnt that. no, i cant point the vision side at my own ollama, which i would have preferred. their argument is its a specific finetune they control. fair, not going to fight it.

before paying for it i did try to roll my own. screencapture-cli + tesseract + a local embedding model into sqlite, got to toy level over a weekend. what i couldnt match alone was the cross-app event correlation (knowing which slack thread i was on when reading a specific commit) and the index updates destroying my battery once the corpus got real. ended up writing more memory plumbing than i wanted to. decided id rather spend that time on actual contract work.

what i use it for is the cross-repo thing. reading three different inference server implementations comparing how they handle continuous batching, and two weeks later trying to remember which one had speculative decode wired in cleanly. used to lose those mid-day. its not perfect, sometimes the search-side model hallucinates a snippet that isnt actually in the history, occasionally returns an answer in korean for no obvious reason, classic LLM stuff. but its better than the bookmark folder i used to maintain.

This stack isnt as smart as the frontier APIs. claude opus 4.7 is still better at long context reasoning than my local setup by a real margin. but for 80% of my actual work the gap doesnt matter, and the 20% where it does i just dont send sensitive stuff to the API anymore. for non sensitive stuff i still hit the API directly from a clean machine.

The meta point is that going local on the model is the easy part. its everything around the model that takes commitment. if youve done the LLM part already, the rest is just one tool at a time.

Quick context: M3 max 64gb, currently running llama 3.3 70b q4 as my daily driver via ollama, qwen3 coder 30b for code (switched from qwen2.5 earlier this year), mlx for the smaller stuff. tried llama 4 scout earlier this year but 64gb is too tight at q4 once you leave headroom for context, sticking with 3.3 70b until i upgrade hardware. moved off the claude api around september last year. the cost wasnt the main reason, the data was. i do contract work with NDAs and i was getting uncomfortable about how much code and how many internal docs were going through anthropics servers.

Heres the thing i didnt expect. once i moved my LLM local, everything else upstream and downstream of the LLM started feeling wrong by comparison.

Example. i was on rewind.ai for a long time because the screen memory thing is genuinely useful for me. then meta bought them in december and the mac app went away, like everyone elses. tried a couple of the diy alternatives that picked up steam after, screenpipe in particular, didnt quite stick for me.

I was using granola for meeting notes. same story, transcripts go up. uninstalled.

I think a lot of you are running into this same thing if youre being honest. you spend 6 months building a private LLM stack, and then you realize half the productivity stuff orbiting your work is still shipping your raw artifacts up to a saas backend, full meeting transcripts, full doc bodies, sometimes whole screen frames, and you havent gone local on the data side at all. youve only gone local on inference. those are two different fights and most people havent had the second one yet. and even on the data side its a spectrum, not a switch, "raw never leaves" is a different bar than "no packets out at all", and you have to actually decide which one is your threshold before you can pick tools.

What i ended up with as of now:

ollama for inference, llama 3.3 70b q4 for general, qwen3 coder 30b for code, nomic-embed-text-v2 for vectors
local whisper.cpp for transcription, the cpu lift is fine for my use
obsidian, plain markdown, encrypted volume on disk
AirJelly for screen and cross app memory, the one item in this list that isnt strictly local, caveats below

i was most skeptical about that last one. to be upfront, airjelly isnt pure offline. captures themselves are stored on disk locally, but the vision model inference round-trips to their backend with just the cropped frame it needs to analyze. raw screen history doesnt leave the machine, which was my actual threshold. if your bar is "literally no packets out" this isnt that. no, i cant point the vision side at my own ollama, which i would have preferred. their argument is its a specific finetune they control. fair, not going to fight it.

before paying for it i did try to roll my own. screencapture-cli + tesseract + a local embedding model into sqlite, got to toy level over a weekend. what i couldnt match alone was the cross-app event correlation (knowing which slack thread i was on when reading a specific commit) and the index updates destroying my battery once the corpus got real. ended up writing more memory plumbing than i wanted to. decided id rather spend that time on actual contract work.

what i use it for is the cross-repo thing. reading three different inference server implementations comparing how they handle continuous batching, and two weeks later trying to remember which one had speculative decode wired in cleanly. used to lose those mid-day. its not perfect, sometimes the search-side model hallucinates a snippet that isnt actually in the history, occasionally returns an answer in korean for no obvious reason, classic LLM stuff. but its better than the bookmark folder i used to maintain.

This stack isnt as smart as the frontier APIs. claude opus 4.7 is still better at long context reasoning than my local setup by a real margin. but for 80% of my actual work the gap doesnt matter, and the 20% where it does i just dont send sensitive stuff to the API anymore. for non sensitive stuff i still hit the API directly from a clean machine.

The meta point is that going local on the model is the easy part. its everything around the model that takes commitment. if youve done the LLM part already, the rest is just one tool at a time.

I have 40 Suno songs sitting in a folder doing nothing. Some are genuinely good. Every time I try to promote one it's just an audio link, and nobody clicks audio links. I kept telling myself I'd learn to edit video but I'd rather spend that time making more music.

A friend asked me last month: can you make a music video with AI? He'd seen stuff on TikTok and wanted to know if it was legit. So I went down the rabbit hole. Tried a couple of general purpose text to video generators. Typed in prompts describing scene by scene. Output had zero relationship to the actual music. Cuts would land in the middle of a vocal phrase. Energy stayed flat through the chorus. Three hours on one song, basically a slideshow with extra steps.

Tried Freebeat after someone mentioned it here. Pasted my Suno link, picked Storytelling mode, got a storyboard. A couple scenes were weird. One had a random crowd shot that didn't match the lyrics, another clashed in style. Regenerated those, second attempt was better. Total 12 minutes from paste to a finished suno music video.

Honest take: output is clearly AI generated. Faces get a little soft sometimes, one transition felt abrupt. If you're expecting something that looks like it cost five grand to produce, recalibrate. But compared to my three hour prompt typing disaster, cuts actually landed on beat and the visual intensity ramped during the chorus.

Posted on YouTube and got more clicks in two days than my audio only uploads usually get in a month. Done three more songs since. Quality varies. One looked cinematic, another generic. Inconsistent but fast enough I don't mind rolling the dice.

The bottleneck with Suno was never the music. It's everything after.

Eight years in and im starting to think cat wu is right. the job used to be roadmaps, specs, keeping engineers and business from killing each other. that was most of it.

Now features ship in days not months. team principles doc means engineers decide obvious stuff without me. metrics readouts are automated. the coordination layer i spent years building is just... infrastructure now.

So what am i actually for. i used to think it was translation, speaking both languages. but i tried this coding tool for a side thing last month, verdents plan mode, and the questions it asked about scope were sharper than half the prds ive written. made me realize i was rushing through the part that actually mattered.

What replaced all the process work is taste. knowing what to build, when its good enough, which user pain is real versus noise. you cant template that. cant document it in a jira ticket.

The pm role isnt dying but its definitely shrinking in some directions and expanding in others. if youre still perfecting your roadmap template, maybe look upstack instead.

I've been mixing and mastering audio for about 12 years now, and I've had moderate sensorineural hearing loss in both ears since my mid-20s — probably from a combination of genetics and not being careful enough with monitors early in my career. The irony isn't lost on me.

The thing that finally pushed me down this rabbit hole wasn't work though. It was Thanksgiving dinner last year. Fifteen people around a table, dishes clanking, three conversations happening at once, and I just... checked out. Smiled and nodded for two hours. My wife filled me in on what I missed in the car ride home. That feeling — being physically present but conversationally absent — I know a lot of you understand it.

So I started digging into the problem the way I'd approach any audio engineering challenge: what's the signal, what's the noise, and what are the tools doing to separate them?

The traditional approach: directional microphones

Most hearing aids for the last couple decades have used dual-microphone arrays to create directional pickup patterns. The idea is straightforward — two mics spaced a few millimeters apart on the device, and the processor uses the tiny time-of-arrival difference between them to attenuate sounds coming from the sides and behind you while preserving what's in front. It's basically beamforming, same principle as a shotgun mic but miniaturized.

This works reasonably well in controlled situations. Speaker in front of you, noise source off to the side, done. But a restaurant? The person you're talking to is at your 10 o'clock, someone else you want to hear is at your 2 o'clock, and the noise is literally everywhere — reflections off hard walls, kitchen clatter, the table next to you. Spatial filtering kind of falls apart when the acoustic scene is that complex.

The newer approach: neural network-based voice separation

This is where things get interesting from an engineering standpoint. Instead of trying to solve the problem spatially, some newer systems are training deep neural networks on massive datasets of mixed audio — clean speech layered with thousands of real-world noise profiles — and the model learns to identify the spectral and temporal characteristics of human voice as distinct from everything else. It's not asking "where is the sound coming from?" It's asking "does this sound like a human voice?"

If you've ever used noise suppression on a Zoom call (like Krisp or the built-in one in Teams), you've experienced a simplified version of this. But doing it in a hearing aid is a completely different engineering problem because of latency. On a video call, 30-40ms of processing delay is invisible. In a hearing aid, anything over about 10ms and you start getting a perceptible disconnect between lip movement and sound, which actually makes speech comprehension worse. So the model has to be tiny, efficient, and fast.

I've been wearing an ELEHEAR Beyond Pro for about four months now — partly because it was in my budget as an OTC device for my mild-to-moderate loss, and partly because I was genuinely curious about their VOCCLEAR system, which they describe as AI-based voice enhancement. From what I can gather, it's doing something in this neural-network voice separation category rather than relying purely on spatial filtering.

What I've actually noticed

I went back to a similar restaurant situation — not Thanksgiving-level chaos, but a busy Saturday night, maybe 70dB ambient. And honestly, the difference in voice clarity compared to my old pair (basic directional mics, no "smart" processing) was noticeable. Conversation across the table came through with more definition, like the consonants had more presence. The background noise was still there but it felt pushed back in the mix, if that makes sense — like someone had pulled down the room mic fader a few dB while keeping the close mics up.

But I want to be honest about the limits too. When the ambient level really cranked up — a birthday party group got loud at the next table, probably pushing 80dB+ — it struggled. Voices started getting that slightly processed, almost compressed quality that tells me the algorithm is working hard and hitting its ceiling. And in a situation where someone was talking to me from behind while I was facing a noisy kitchen, it clearly couldn't resolve that well. The spatial component still matters.

Also want to be clear: I have mild-to-moderate loss. These OTC devices are designed for that range. If your loss is more severe, this isn't the conversation — you need proper audiological care and prescription-fit devices. The processing is impressive for what it is, but it's not magic.

The question I keep coming back to

I think we're in this interesting transitional period where the DSP in hearing aids is shifting from traditional signal processing (beamforming, static noise reduction, compression) toward learned models that can make more nuanced decisions about what's speech and what isn't. But I'm curious — how much of this are people actually experiencing in practice?

What are you all using in noisy environments, and does it work for you? Has anyone else gotten curious about what's actually happening inside their devices when the noise picks up? Or do you have non-device strategies (seating position, FM systems, just avoiding restaurants altogether) that work better than any algorithm?