u/InterviewDesigner777

I don’t understand where he went and where to do my riffs now ?

Help 🆘

It was very warm from the very morning, not hot as it happens in August, but warm enough for shorts and a T-shirt to be clothes according to the weather.

We cooked food for ourselves and went to the river. It was not a long hike, enough for a walk and to explore the surroundings.

Continuation in the comments

What to expect from ai developers and tools in the next six months - a year?

Quality improvement? New customers and models? New players in the market of ai and collaboration.

What are your assumptions? Let’s try to understand the vector of development and emergence of new products in this silicon world.

Sketch your options )

We’ll find out the correct answers in due course, in a year

What to expect from ai developers and tools in the next six months - a year?

Quality improvement? New customers and models? New players in the market of ai and collaboration.

What are your assumptions? Let’s try to understand the vector of development and emergence of new products in this silicon world.

Sketch your options )

We’ll find out the correct answers in due course, in a year

What to expect from ai developers and tools in the next six months - a year?

Quality improvement? New customers and models? New players in the market of ai and collaboration.

What are your assumptions? Let’s try to understand the vector of development and emergence of new products in this silicon world.

Sketch your options )

We’ll find out the correct answers in due course, in a year

Random acts of kindness often make the best stories. Have you ever helped someone you didn't know? Or has a stranger ever helped you? Share your story.

Most people stop at the first answer and think that's the real reason. It usually isn't.

Ask "why do I want this?" Write down the answer. Then ask "why" again about that answer. Keep going for 5-7 rounds.

Here's what that can look like:

"I want to get promoted." Why? "I'll make more money." Why does that matter? "I'll feel more secure." Why do you need that? "I'll finally feel like I'm not falling behind." Why does that matter? "Because I want to feel like I'm in control of my life." Why? "Because for a long time, I didn't feel like I was."

Notice how far that ended up from "money."

Most people give up on goals not from lack of motivation, but because they never really knew why they wanted them in the first place. Once you know the real reason, it's a lot harder to quit.

I've been following the recent pull requests and saw that the desktop app is being transformed from a chat-only interface into a full management tool with a tabbed settings UI, a model manager, and a monitoring dashboard.

I'm curious about what's next for the project. Is there an official or public roadmap where we can see the planned features for the interface or other major functionalities? Also, what new features are you most looking forward to?

Here are a few things I'd love to see planned:

• UI/UX: Further improvements to the desktop app or official web interface.
• Features: Hot-swappable models without server restarts or more built-in tools for the new agent experience.
• Cloud & Hybrid: Expanding the new hybrid local/cloud capabilities.

Thanks for any insights!

When I got home, the freezer was broken. It was really hard to carry the watermelon home, and now looks like I'll have to eat it all in one sitting.

Do you ever have moments like this — when expectations turn into challenges? 😄

I struggled with executive dysfunction for years. Every day was a list of 'shoulds' – I should work out, I should reply to emails, I should clean.
Then my therapist pointed out that 'should' implies guilt and external pressure. It puts you in a defensive mindset.
But 'could' implies choice and opportunity. When you say 'I could go to the gym today', your brain starts thinking about *how* to do it, rather than *why* you don't want to.
I tried it for 30 days. I still don't do everything, but the mental resistance is 80% lower. Anyone else tried this cognitive reframing?

I struggled with executive dysfunction for years. Every day was a list of 'shoulds' – I should work out, I should reply to emails, I should clean.
Then my therapist pointed out that 'should' implies guilt and external pressure. It puts you in a defensive mindset.
But 'could' implies choice and opportunity. When you say 'I could go to the gym today', your brain starts thinking about \*how\* to do it, rather than \*why\* you don't want to.
I tried it for 30 days. I still don't do everything, but the mental resistance is 80% lower. Anyone else tried this cognitive reframing?

I struggled with executive dysfunction for years. Every day was a list of 'shoulds' – I should work out, I should reply to emails, I should clean.
Then my therapist pointed out that 'should' implies guilt and external pressure. It puts you in a defensive mindset.
But 'could' implies choice and opportunity. When you say 'I could go to the gym today', your brain starts thinking about *how* to do it, rather than *why* you don't want to.
I tried it for 30 days. I still don't do everything, but the mental resistance is 80% lower. Anyone else tried this cognitive reframing?

Same unit of work. Same million output tokens. The only difference is which company's logo is on the box:

Model	Output, $/1M
DeepSeek V4 Flash	$0.28
DeepSeek V4 Pro	$0.87
GPT-5.6 Luna	$1.20
Claude Sonnet	$3.00
GPT-5.6 Terra	$9.00
Claude Opus 4.8	$25.00
Fable 5	$50.00

One of these is a luxury tax, and it's not the one that costs $0.28. The gap between the best value and the most expensive is 178× — for the same token, doing the same work.

V4 Flash 0731 is a gift, honestly. We're living in the good timeline.

Someone in a comment here — I can't find it anymore — said to try quantizing the KV cache instead of buying more VRAM. Everyone in this sub says never quantize the KV, it wrecks quality. So I put it off for weeks.

Finally measured it on my 2× Radeon R9700 (Syosse-CH, RDNA4), Qwen3.6-27B at Q4_K_M. And... wow.

Same GPU, same weights, same model — I went from ~32k to ~80k usable context just by switching the KV cache from BF16 to Q8_0. No second GPU, no RAM spill.

Here's the whole picture for a 27B at Q4_K_M (weights ≈ 15.5 GB):

| KV cache | VRAM @ 120k | Total | Fits 2× R9700? | Quality |

|---|---|---|---|---|

| BF16 | ~9-10 GB | ~25 GB | Only to ~32k | Reference |

| Q8_0 | ~5 GB | ~20.5 GB | Up to ~80k | Can't tell from BF16 |

| Q4_0 | ~2.5 GB | ~18 GB | Yes | Falls apart past ~64k |

I can't reliably tell the difference between Q8 KV and BF16 up to ~80k. It's not the "night and day" quality drop I was promised. It's the opposite — I was throwing away 2.5x context for nothing.

Q4 KV is a different story though. That one I can feel past ~64k — the model starts losing track of early context details in long-doc RAG.

Thank you, whoever wrote that comment. You saved me from buying a second GPU.

EDIT: didn't expect anyone to care, but since a few asked — flags are -ctk q8_0 -ctv q8_0 on llama.cpp (Syosse-CH RDNA fork, same flags work).

Where it breaks:

- Agentic loops that re-read context repeatedly: quantization errors compound every pass. Q8, not Q4.

- 8B and smaller models: lean harder on KV fidelity, Q4 hurts noticeably earlier.

- If you're already RAM-spilling: quantized KV shrinks what spills, but measure tokens/s first — spill + long context can halve throughput.

Anyone else running quantized KV on AMD? How long is your context, and where did you hit the quality wall?

I keep seeing the "use a big model via API as the architect, run local small/mid models as workers" pattern recommended for people with modest local hardware. I've been running it myself (orchestrator on a hosted model, local Qwen-class 27B workers doing scans/refactors/test runs), and it works - but I have a nagging feeling the win is smaller than the hype once you account for round-trip latency and the fact that the orchestrator still has to read everything the workers produce.

What I'd actually like to see from this sub: has anyone measured, with real numbers, the point where the local worker becomes the bottleneck vs. where the orchestrator's reasoning is genuinely load-bearing? Specifically:

• token/s on your local card when the worker is doing high-volume boilerplate vs. when it's doing judgment calls
• whether the orchestrator-via-API + local-worker setup actually beats just running a bigger local model end-to-end (if your VRAM allows)
• failure modes you hit that pure "all local" or "all API" didn't have

Not looking for a recommendation - I want data/war stories. If you've A/B'd it, what changed your mind?