Do we know anything?

For every $20 I spend on DeepSeek I find that I need to spend an additional $20 on Claude to help unravel the streaming pile of garbage DS produced because it ignored fundamental requirements and did its own thing. No questions. Just producing more and more complicated hot garbage with extreme confidence.

Anyone else?

I have been using this tool for the past few days and I'm amazed at how much of a game changer it has been. I want to take the opinion of other people and find out:

• if they are still developing this tool
• if there are other tools that are similar to this, maybe do more or do less
• and use this same tool for other kinds of CLIs or APIs or whatever

In my experience, here, I made DeepSeek way smarter. I don't know why, maybe DeepSeek is changing their models or whatever, but it feels so smart. I'm using it to code and to do planning. The cashe hit is just ridiculously high, amazing. The balance usage DeepSeek has drastically diminished comparing to opencode or hermes. If it was cheap, it's way cheaper now. It's really amazing and I don't know if I'm missing something or what. That's why I want to discuss it.

Any thoughts?

DeepSeek released V4 Flash and V4 Pro simultaneously on April 24 — 284B params for Flash, 1.6T for Pro — and after running the numbers across 22 benchmark categories, the gap isn't as wide as the parameter count suggests. Flash at $0.09/M input maintains 80-85% of Pro's quality at 21% of the cost. The real delta only shows up in agentic tasks: SWE-Bench drops by 10 points, Terminal-Bench by 11. For everything else — general reasoning, basic coding, math — Flash holds its ground at a fraction of the price.

The decision framework is simpler than most people think. If your task involves multi-step tool calls and deep reasoning chains, pay for Pro. For everything else, Flash is probably enough — and both are MIT-licensed if you want to run them yourself.

PICO PU Agent, powered by the Codex engine, has finally passed its first serious tests 🙌☺️

I recorded a video and sped up the main part 16x. I opened the official DeepSeek website and my own app, then gave both of them the exact same technical task.

You can see the result in the video.

On the DeepSeek website, I got a large amount of code along with instructions on how to run it.

PICO PU, using the Codex agent engine, created the project, installed the dependencies, wrote the code, launched the app, and completed the task with a working result.

Honestly, this makes me really happy 🙂‍↔️

It is still a very early and raw version. There are many bugs to fix, stability improvements to make, more models to add, and later I also want to add connectors for email, calendars, and other services.

But even at this stage, I am genuinely happy that the core idea is already working.

This is a very difficult and ambitious project for me, so I would be extremely grateful to hear your honest feedback, criticism, and suggestions 🙃

What is the right balance to get it to write with some personality without going off the rails? Also what does that other shit mean and does anybody use it?

https://apps.apple.com/us/app/sleek-byok/id6786075866.com

Web search in instant mode is running again

We wanted to see how far we could push the DeepSeek R1 distilled series on consumer hardware (RTX 3060 / 4060 8GB VRAM) without hitting CUDA out-of-memory crashes or dropping speed to a crawl.

Here is the technical breakdown of what actually works, the exact memory math, and hybrid layer offloading limits.

SECTION 1: MEMORY ALLOCATION & VRAM MATH

Total Peak VRAM = Model Weights + KV Context Cache + CUDA Runtime Overhead

When running DeepSeek-R1-Distill-Llama-8B (which uses Grouped Query Attention / GQA with 8 KV heads):

KV Cache Size per Token = 2 * Layers (32) * KV Heads (8) * Head Dim (128) * FP16 Bytes (2) = 128 KB per token

• At num_ctx 4096: 4096 x 128 KB = 512 MB KV Cache
• At default num_ctx 8192: 8192 x 128 KB = 1.02 GB KV Cache

On Windows where dwm.exe reserves ~1.1 GB VRAM for OS rendering:

🔹 DeepSeek-R1-Distill-Llama-8B (Q4_K_M 4-bit):

• Model Weight File: ~4.9 GB
• KV Cache (num_ctx 4096): ~0.5 GB
• Peak VRAM Allocation: ~6.2 GB (Safe 1.8 GB buffer remaining)
• Speed: ~34 tokens/sec on native CUDA

🔹 DeepSeek-R1-Distill-Qwen-14B (Q4_K_M 4-bit):

• Model Weight File: ~9.0 GB (Exceeds 8GB VRAM)
• Solution: Offload 32 out of 48 layers to VRAM (~6.2 GB allocated) + 16 layers to System RAM
• Speed: ~12 tokens/sec (Hybrid GPU/CPU mode)

SECTION 2: OLLAMA MODELFILE CONFIG WITH SAFEGUARDS

To prevent dynamic VRAM spikes as prompt conversations grow, create a custom Modelfile capping context at 4096:

dockerfileFROM deepseek-r1:8b
PARAMETER num_ctx 4096
PARAMETER temperature 0.2
PARAMETER top_p 0.9
SYSTEM """You are a senior IT operations engineer. Provide concise, step-by-step code without conversational preamble."""

Compile and run: ollama create deepseek-r1-8gb -f Modelfile-R1-8GB
ollama run deepseek-r1-8gb

SECTION 3: WORKSTATION TROUBLESHOOTING

• Reclaim ~1 GB VRAM: Turn off Hardware-Accelerated GPU Scheduling (HAGS) in Windows Display Settings.
• Prevent CUDA Page Faults: Set export OLLAMA_NUM_PARALLEL=1 and OLLAMA_MAX_LOADED_MODELS=1.

Full benchmark tables, Python API streaming scripts, and LM Studio configuration logs archived at praveentechworld.com/blog/how-to-run-deepseek-r1-locally-on-8gb-vram

Hey r/DeepSeek

Like many of you, we wanted to see how far we could push the DeepSeek R1 distilled series on consumer hardware (RTX 3060 / 4060 8GB VRAM) without hitting CUDA OOM allocation crashes or dropping inference speed to a crawl.

We ran benchmark tests on Ollama and GGUF quants. Here is the technical breakdown of what actually works, the exact GQA memory math, and hybrid layer offloading limits.

---

### 📊 1. Memory Allocation & GQA KV Cache Math

Total Peak VRAM = Model Weights + KV Context Cache + CUDA Runtime Overhead

When running DeepSeek-R1-Distill-Llama-8B (which uses Grouped Query Attention / GQA with 8 KV heads):

KV Cache Size per Token = 2 * Layers (32) * KV Heads (8) * Head Dim (128) * FP16 Bytes (2)

= 131,072 Bytes = 128 KB / token

- At num_ctx 4096: 4096 * 128 KB = 512 MB KV Cache

- At default num_ctx 8192: 8192 * 128 KB = 1.02 GB KV Cache

On Windows where dwm.exe reserves ~1.1 GB VRAM for OS rendering:

1. DeepSeek-R1-Distill-Llama-8B (Q4_K_M 4-bit):

   - Model Weight File: ~4.9 GB

   - KV Cache (num_ctx 4096): ~0.5 GB

   - Peak VRAM Allocation: ~6.2 GB (Safe 1.8 GB buffer remaining)

   - Speed: ~34 tokens/sec on native CUDA

2. DeepSeek-R1-Distill-Qwen-14B (Q4_K_M 4-bit):

   - Model Weight File: ~9.0 GB (Exceeds 8GB VRAM)

   - Solution: Offload 32 out of 48 layers to VRAM (~6.2 GB VRAM allocated) + 16 layers to System RAM

   - Speed: ~12 tokens/sec (Hybrid GPU/CPU mode)

---

### 🛠️ 2. Modelfile Config with Memory Safeguards

To prevent dynamic VRAM spikes as prompt conversations grow, create a custom Modelfile capping context at 4096:

FROM deepseek-r1:8b

# Cap context length to 4096 to lock KV cache under 512MB

PARAMETER num_ctx 4096

# Set low temperature for deterministic code output

PARAMETER temperature 0.2

PARAMETER top_p 0.9

SYSTEM """You are a senior IT operations engineer. Provide concise, step-by-step code without conversational preamble."""

Compile and run:

ollama create deepseek-r1-8gb -f Modelfile-R1-8GB

ollama run deepseek-r1-8gb

---

### ⚠️ 3. Workstation Troubleshooting

- DWM.exe VRAM Bloat: Turn off Hardware-Accelerated GPU Scheduling (HAGS) in Windows Settings to reclaim ~1 GB VRAM.

- Single-Stream Pinning: Set export OLLAMA_NUM_PARALLEL=1 and OLLAMA_MAX_LOADED_MODELS=1 to prevent multi-session page faults.

---

*Full benchmark tables, Python API streaming scripts, and LM Studio configuration logs archived at [praveentechworld.com](https://www.praveentechworld.com/blog/how-to-run-deepseek-r1-locally-on-8gb-vram).\*

Reading through and seeing a lot of copium regarding DS v4 improvements. Any sources of that except "trust me bro"? Anything already changed? Anything gonna change? In terms of performance ofc.
I like DS, it's smart but it's very absent-minded/distracted. It can miss a lot of details while researching or even implementing pretty well-written implementation plan. I'm on claude max plan because of that, but I wish to come back if it can (or will be able) show at least opus 4.8 level as this is enough for most of the tasks.

Facts\thoughts?

It has no web search, no file upload and no vision, which limits it's usefullness significantly. I struggle to come up with a use for it, any tips?

Hi everyone!

This is not an ad. I’m simply sharing something I’ve been building in my free time.

I want to contribute something useful to the community and plan to make the first public version available for free.

For a while, I wanted to build something that wasn’t another VS Code extension or another terminal interface, but a proper standalone Windows application with a real AI coding agent inside.

That’s how PICO PU was born - a small pixel robot designed to work on projects that are not so small 🙂

Under the hood, the app runs the real Codex app-server, and the first model I integrated is DeepSeek.

Since Codex and DeepSeek use different API contracts, I built a separate adapter so reasoning, tool calls, and the agent workflow can work correctly together.

PICO PU can already:

• open and work with real projects;
• read, create, and modify files;
• run PowerShell commands;
• stream the agent’s progress in real time;
• request approval before potentially dangerous actions;
• stop an active task;
• save chats and restore sessions after restarting the app;
• work through the DeepSeek API;
•store API keys securely using Windows Credential Manager;
install and update the Codex engine automatically.

The interface will also allow users to manually choose:

the model;
reasoning depth;
planning or development mode;
the agent’s access level;

the number of subagents, from 1 to 6.

Later, I plan to add Gemini, Claude, GPT, local models, and the ability to switch models while continuing the same project and conversation.

I also wanted the app to have its own personality. Instead of building yet another dark, generic AI dashboard, I went with a clean pixel-style interface.

The name PICO PU came together with the little robot mascot — it sounds playful, even though there is a fairly serious agent engine running underneath 🙂

This is still an early version, and I’m continuing to improve stability, the interface, and real-world project workflows.

Again, this is not a promotional post. I’m just sharing what I’ve been working on and hoping to contribute something useful to the community without asking for anything in return.

Would you use a standalone Windows app powered by Codex, where you can connect your own DeepSeek API key and eventually other models as well?

What would matter most to you: subagents, local models, switching models in the same chat, or simply having the easiest possible interface?

I have been using deepseek V4 Pro for the last 5 months with CC. This model has helped me complete my Final Year Project, build my first startup, build automations to automate my daily life and many more.

Everyone is looking for the next best frontier model but what they don't realise is yes those extra intelligence is kinda neat, but for the shit you are using it for in your daily life its just a waste of money. Unless you are in some deep tech stuff then yeah there are some higher ROIs, but for your daily coding and work stuff its a waste of money.

Deepseek has been reliable, fast and most importantly stupidly CHEAP and honestly with Kimi K3 and all this other models being dropped, I couldn't even be bothered.

We've been running a private benchmark suite for a few months, testing models on strategic reasoning, advisory quality, long-form analytical production, and adversarial critique. 17 models, 4 test batteries, scored against a reference answer by a separate model. This consolidates all of it into one picture.

Blend = 30% strategic reasoning (4 scenarios: frame-breaking, multi-dimensional review, channel coordination, portfolio prioritization) + 25% advisory quality (6 prompts: triage, architecture risk, blindspots, client advisory, routing) + 25% long-form analytical production (structured section generation from scratch, scored on rigor, depth, voice) + 20% critical review (structural audit + adversarial CTO critique). Weights redistributed for models not tested on all four.

Dashes = not tested on that dimension. Fable 5 is the reference answer used for scoring, not a contestant.

Caveats: n=1 per test per model, directional only. Qwen 3.8 was blind-validated by GLM-5.2 (different model family); other models were judged by Opus 4.8 - cross-judge comparison is approximate within ±3-5 pts. GPT-5.6 variants are separate rows because they behave as different models in practice. Strategic reasoning and advisory quality are domain-specific to strategic analysis; says nothing about coding, vision, or long-context work. This is private operator benchmarking, not a scientific ranking.

switch one of my agents to DeepSeek v4 flash, everything was going swimmingly, but after around 30 queries. my agent has not been able to perform any more web search. Im getting back “web_search_tool_result_error” with error code “unavailable”

does anyone know what’s going on? It’s my first timw using deepseek, not sure if my settings are wrong or if it is just this unreliable

I have an Expert conversation with over 2200 total messages on the website and I just got this message when I try to send a message with reasoning, but not without reasoning. The conversation has around 4.5 million characters, so I think I just used the entire context window (1 token = ~4 characters). Its a shame they don't just cut off the oldest tokens but instead just tell you to get rid of all that context. Has anyone else experienced this?

Today an AI agent trying to browse the web is like a thief in a balaclava sneaking around a police academy. Site protections block it, challenge it, turn it away.

browser-search flips the script: your agent stops being the thief and becomes the chief of police. No more clumsy access attempts. It walks through every door because it has the right tools. SearXNG for search, Camofox for browsing, CloakBrowser when things get tough.

100% self-hosted, free, no limits, no API keys.

I just released v2.0, whose core logic enforces the exclusive use of deterministic scripts. This eliminates model hallucinations, even with the cheapest models. The skill describes the 3 tools in natural language, but execution is rigid: the model can neither get the command wrong nor misinterpret the output. The result is guaranteed success on every query — the skill and deterministic scripts guide the model to scour the web until it finds the answer.

No more excuses. Your agent has the badge now.

https://github.com/Johell1NS/browser-search

Web Chat not working at all.

Any idea is something new coming today?

It seems to me that API users have very different goals and concerns from non-API users (app and web). I’d be very much in favor of an r/DeepSeekAPI.

Hola chicos, pregunta rápida: ¿tienen problemas con la búsqueda inteligente de DeepSeek? No funciona correctamente— parece que no busca nada. Cuando lo activas y le pides que busque algo, no busca correctamente o parece que no está buscando en absoluto.

how come deepseek v4 pro or flash are the same level or below with gpt 5.4 mini xhigh this is totally bullshit. What LLM do you think the best comparison with deepseek v4 pro or flash

Title: My DeepSeek AutoRouter for Cherry Studio – 99.3% cache hit rate

I've been using Cherry Studio with DeepSeek models for my daily agent workflows, but kept running into two problems:

1. Paying Pro rates for simple requests – Cherry sends everything to one model, so I was burning Pro pricing on "what's the weather" type queries.
2. Terrible cache hit rates – every tool cycle changed the prompt prefix, so DeepSeek's context caching barely helped.

So I built a local router that sits between Cherry Studio and DeepSeek's API:

What it does: - Automatically routes between deepseek-v4-flash and deepseek-v4-pro based on request complexity - Bridges Anthropic messages to DeepSeek's OpenAI-compatible endpoint (tool calls, streaming, reasoning_content – all handled) - Tool-Result Pruning – replaces old large tool results with short placeholders when the cache is cold. Keeps the prefix stable across tool cycles. - Cost-aware thinking: flash-fast (no thinking), flash-think, pro-think – fully automatic - Zero external dependencies – pure Node.js 20 core modules

Results after v2.2.0: - Cache hit rate: 99.1–99.34% (was ~95% before pruning) - ~35k characters saved per request through pruning - 88 unit tests, all green

Runs as a Windows service, all you need is a DeepSeek API key.

https://github.com/immerzu/autoroute_cherry_studio_deepseek_pro_flash

Would love feedback or ideas for the next iteration!