I had:

- Face unlock enabled for lockscreen

- Fingerprint enabled only for apps (NOT for lockscreen unlock)

Now whenever I try to:

- enable fingerprint for lockscreen

- change biometric settings

- use Google Password Manager/autofill in some cases

the phone asks for biometric verification AFTER already accepting my PIN/password.

Problem is: the fingerprint is not authorized for lockscreen/device unlock, so it fails for system-level authentication even though fingerprint still works inside apps/app lock.

So I’m stuck in a circular state where Android wants fingerprint auth to change fingerprint settings.

Things that still work:

- PIN/password

- face unlock

- fingerprint inside apps

Things that don’t:

- changing biometric settings

- enabling fingerprint for lockscreen

- some credential manager/auth flows

Things already tried:

- multiple reboots

- unlocking with PIN only after reboot

- biometric lockout attempts

- recovery mode

- clearing Google Play Services data

- clearing Credential Manager cache

- checking Autofill/Password Manager settings

No change.

Has anyone seen this on Nothing OS / Android 16 or knows a workaround without factory reset?

So I've been going down a rabbit hole lately and I can't find many people actually talking about this specific use case.

everyone here runs local LLMs for coding, chat, maybe some creative writing. cool. But what about using it as a proper personal knowledge base? like, dump your own notes, PDFs, random docs into it and actually query your own life privately, every day.

I tried looking into this seriously and hit a wall. Most resources either assume you're a developer building something, or they're 2 years old and recommend tools that have completely changed since.

So genuinely asking, is anyone here actually doing this day to day? Not as an experiment, but as a real workflow?

Things I keep running into that I can't figure out:

• What model are you running for this? RAG on consumer hardware seems finicky depending on quant
• Do you actually trust the retrieval or do you double check everything because hallucinations?
• LlamaIndex vs Ollama vs whatever else has anything actually made this less painful recently?
• Context length, how do you handle it when your personal docs start piling up?

Not looking for a tutorial or a GitHub repo. Just want to hear from someone who's made this work without it becoming a part time job to maintain.

mirror reflects everything back directly, so why are left and right reversed but not up and down? Why??

I’ve tried several IPTV providers over the last few months, mainly to see which ones stay reliable after the initial subscription period.

Most services start strong. Fast loading channels, huge libraries, and smooth streams make everything look perfect at first.

But after regular use, especially during live sports, the difference between good marketing and real server quality becomes obvious.

The Problem With Most IPTV Providers

A lot of IPTV services focus on selling massive channel lists and low pricing.

What they don’t mention is how badly performance drops during high traffic periods.

The most common problems I experienced were:

• Streams buffering during live games

• Long delays opening channels

• Random freezing mid-match

• Inconsistent HD and 4K quality

The issue usually comes down to overloaded servers

What Makes a Service Reliable

After testing different providers, I realized the most important things are:

• Stable performance during busy hours

• Fast and smooth channel switching

• Consistent stream quality

• Reliable uptime during major events

That matters far more than having thousands of extra channels nobody even watches xd

Why Flixaria Stayed Consistent

Among all the IPTV services I tested, Flixaria was the one that stayed the most reliable over time.

Their Anti-Freeze 10.0 infrastructure handled heavy traffic far better than most competitors.

What stood out most:

• Quick channel loading without delays

• Smooth sports streaming during peak hours

• Stable high-bitrate 4K playback

• Reliable performance week after week

Final Verdict

Most IPTV services look good for the first few days.

The real difference appears once peak traffic hits and thousands of users start streaming simultaneously.

In my experience, Flixaria has been one of the few providers in 2026 that actually maintains stable long term performance, instead of relying purely on marketing

tech writer at a mid-size SaaS company. our doc backlog has been a running joke for two years. 47 articles in various states of "someone should update that." I finally made a dent this quarter and wanted to share what actually changed.

the bottleneck was never research or knowledge. I sit in on product calls, I know how the features work. the bottleneck was the actual writing. sitting down after a meeting where I just learned exactly how a new integration works and then spending 45 minutes typing up the first draft felt like doing the same work twice.

what I changed: I started capturing first drafts by talking through the documentation right after the meeting. I use an AI voice dictation tool called Willow Voice and just narrate the doc structure out loud. "this integration connects to the user's CRM, requires an API key from the settings page, supports three authentication methods..." takes maybe 3-4 minutes of talking to get a rough 800-word first draft.

the transcription quality is solid enough that my editing pass takes about 15 minutes instead of writing from scratch which was taking 45-60 minutes. the voice-to-text adjusts its tone depending on what app I'm dictating into which is nice when I'm switching between formal docs and quick slack updates to the product team.

real numbers: I went from publishing about 3 articles per week to 6-7 without adding hours. the trick is the first draft is now basically free in terms of time.

the quality concern is valid. spoken first drafts are looser than typed ones. more conversational, more repetitive. but honestly the editing pass catches that and the final output is the same quality. just faster to get there.

anyone else using voice for documentation drafts? curious if this scales for API docs or if it's more of a conceptual docs thing.

So i started with a very normal goal today to make my lit review less painful.

And right now it’s just me, google scholar, and 25 tabs open at once. i keep losing papers, forgetting where a citation came from.

so i thought okay, maybe i should finally try some of these AI tools everyone talks about.

looked into a few… scite, elicit, perplexity pro. they all seem helpful, especially for citations and summaries, but the pricing surprised me a bit. it’s like $20–$50/month each.

and suddenly this “let me be more productive” idea turned into finding a cheaper research AI tool. Yes I am guilty.

but I finally found one scira it’s just 15$ for pro version and it does enough to help me not lose my mind while tracking papers.

but yeah, i’m still not sure what the norm is here. are people actually paying for these tools long term or just testing things and going back to manual workflows?

we delivered to the C-suite of a Fortune 500 client. deck was clean. recommendations were solid. my senior partner even complimented the structure.then the CFO asked one question about the assumptions underlying our revenue model and i just sort of floated off into space. gave a technically accurate but completely incoherent answer. my partner had to smooth it over.i've been on this engagement for six weeks. i know this model inside out. the CFO's question was fair and i knew the answer.i don't understand why the room makes me forget things i literally built.