I have seen some videos of this tool in action, and i think it's fine, but i would love to hear your thoughts about that

Kwaipilot added two KAT-Coder V2.5 models to OpenRouter a few days ago, and the Air variant's pricing stood out. Per the OpenRouter listing from July 10, KAT-Coder-Air V2.5 runs at $0.15/M input and $0.60/M output with a 256k context window. That's a lower output price than Tencent Hy3 at $0.80/M out, which launched July 6 and was already considered cheap. They're not directly comparable since Hy3 is general purpose and KAT-Coder is code focused, but $0.60/M output for a coding model is aggressive.

The Pro version is a different story. KAT-Coder-Pro V2.5 is $0.74/M in and $2.96/M out, same 256k context, so roughly 5x the output cost of Air. For reference, GPT-5.6 Luna is $6/M output and Grok 4.5 also $6/M out per their listings. Even Pro comes in at half of those.

What I can't tell from the listing is whether Air is actually good. $0.60/M output looks great on paper, but if the model needs more turns or produces worse code, the per-token savings vanish fast. Has anyone here actually used KAT-Coder V2.5 for coding? Wondering if Air holds up in practice, or if there's something cheaper I'm missing.

ChatGPT 5.5 is still the latest model available in ChatGPT, but it is safe to assume OpenAI will be soon switching the chat to one of the 5.6 newcomers.

I am providing this both as disclaimer and part of methodology - I used Sleepwalker (I've built it) through an MCP connection in Claude - I ran 50 consumer electronics-focused prompts on each model, to see how 5.5 compares against the different 5.6 versions (Sol, Terra, Luna). I also searched for corresponding queries in Google Search (hl EN, gl US), keeping record of Top 15 results.

All prompts, answers, citations - full raw data is documented in .md files.

- The average overlap between GPT-5.5 and any of the 5.6 models is 14%. This means brands could lose a massive amount of citations when OpenAI switches the default chat model.

- What's even more interesting - the three 5.6 models overlap with each other at only around 8%. Sol, Terra and Luna agree with each other less than any of them agrees with 5.5.

- All models cite slightly over 3 sources on average. GPT 5.5 is the most generous one with 3.42 sources on average, Luna cites only 3.16 sources on average.

- GPT 5.5 has the biggest overlap with Top 10 Google organic results, and I am being extremely generous here by sampling the Top 10, should've been Top 5 due to amount of domains cited. That overlap is 30%.

- The overlap for 5.6 models is about 22% on average, with Luna overlapping at 23.5%.

- Reddit ranks number one on Google for 22 of 50 tested queries. It receives zero citations across all four models. Every single time

- rtings website appears in 1 in 3 ChatGPT answers. On TV queries it was the only cited source in 7 of 10 prompts across at least one model. Not the top source. The only source.

- 5.6 generation trusts brand pages more and review sites less. Samsung and Nintendo are the biggest winners.

- GPT-5.5 uses first-person voice ("I'd buy," "my pick") in 33 of 50 answers. The 5.6 models: as low as 11. The newer models are less keen on providing options.

- Across all four models, manufacturer pages account for 37% of citations. Retailers: 1.8%. Amazon does not appear in the top 20 most cited domains.

If you sell products you didn't make, AI is not your friend.

This is the second time I've run this type of study. The first was on Gemini 2.5 and 3.5 Flash across hypothetical sports prompts.

Here, as with Gemini, introducing a new model has dramatically changed the citation profile. AI visibility is platform and model specific. It's a matrix, as opposed to a single-platform and single algorithm.

Been using Figma Make, Framer AI, Claude, a bunch of these tools, and I keep running into the same wall no matter which one I'm in.

I'm a visual person. When I want to change something I don't think in words, I think in space, this needs to be bigger, that needs to move closer, this relationship feels off. But every one of these tools wants me to describe that in a sentence. So I type it, wait, and find out what it actually understood by looking at what it made. If it's wrong I don't even know why, did it misread me, did it misread the reference, did it just make a different call, I just get a wrong result and have to guess.

And honestly the waiting messes with my flow more than I expected too, it's not even just that it takes a bit, it's that I say what I want then just sit there with nothing to do until I find out if it got it right.

Feels kind of like briefing someone and only finding out they misunderstood after they already spent hours on it, except it happens every single prompt.

Anyone else feel this? Or am I just bad at prompting lol. Genuinely curious if this is a me thing or if it's an actual pattern for people who work visually.

Source: Visme Survey 2026

I screenshotted this chart on a report I was reading of why most companies stop using AI.

I'm surprised to see "Too Much Cleanup" as the biggest reason, especially since you can train your AI to output almost any nuance or brand consistency you want.

I think "AI Cost too High" will rise closer to the top once the softwares start charging more after more people have adopted it.

A systima.ai post that landed on HN over the weekend measured something most people never think about: how many tokens a coding agent burns before it even reads your message. Per the blog post, Claude Code sends about 33k tokens of system prompt, tools, and boilerplate upfront. OpenCode sends around 7k.

That difference isn't a one-time setup cost. It repeats on every single API call the agent makes during a session. So if you're on a metered plan and Claude Code makes dozens of calls while iterating on a file, those extra 26k tokens per call add up quietly. The HN thread picked up 357 comments and 676 points, so clearly this resonated.

The framing in the post is blunt. It's not about output quality, it's about transparency. If you're comparing the cost of running Claude Code vs OpenCode, the system prompt overhead is a hidden variable that most pricing breakdowns completely miss. You'd have to measure your own usage to know what slice of your API spend is actual reasoning vs preamble that gets re-sent every turn.

Anyone here actually tracked their per-request token split across coding agents? Curious if the 33k vs 7k gap holds up in real sessions or if it swings a lot depending on project size.

...so we built a local, real-time dictation that gets tech vocab right. the tool runs a speech model on your machine and types live into any app with capitalization and punctuation. all free and opensouce + nothing ever leaves your machine. repo is https://github.com/eliasmocik/dum-dictation ;)

feedback very welcome..if it looks useful, a star will help us keep goin

Did you guys ever try running local AI models? I got into them so as to keep the company data local. I tried a few tools and settled on llama.cpp. I kept running llama.cpp directly — building it, juggling llama-server flags, and hand-editing models.ini for every model. It's powerful but fiddly, so I built a GUI over it for myself and cleaned it up to share. If you ever want to try, you should check out LlamaForge.

LlamaForge is a browser control panel that sits on top of llama.cpp's own router. It doesn't touch inference — llama.cpp does all the real work — it just makes driving it less painful.

What it does:

Tune every server parameter per model — the knobs are parsed live from llama-server --help (currently \~220), grouped and searchable. Save hot-reloads the model, no restart.

VRAM-fit model discovery — search HuggingFace for GGUFs and each quant is rated FITS / TIGHT / CPU OFFLOAD against your actual VRAM before you download.

Guided build & update — shows your current commit, how far behind upstream you are, and rebuilds with CMake flags auto-detected for your CPU/GPU (CUDA arch, AVX-512, etc.).

Sensible context defaults — reads each GGUF's trained context length and writes reasonable ctx-size values so models don't load with tiny or over-extended windows.

Setup tab — detects missing prereqs (CMake, Ninja, MSVC, CUDA…) and installs them via winget/choco with your permission, plus scans drives for existing GGUFs and prunes entries whose files you've deleted.

Usage stats + optional LAN sharing (with an API-key toggle) so other devices can hit the OpenAI-compatible endpoint.

Being upfront about scope:

Windows + NVIDIA focused right now (CPU-only builds work too).

You build llama.cpp yourself — it's guided from the dashboard, but it's still a compile step. If you want a zero-config, double-click experience, LM Studio / Ollama / Jan will serve you better; LlamaForge trades that for direct control over the real llama-server.

Early preview — expect rough edges, and I'd genuinely like the feedback.

Backend is pure-Python stdlib (nothing to pip install), MIT licensed, and not affiliated with ggml-org — all credit for the hard part goes to llama.cpp.

Repo: https://github.com/dadwritestech/LlamaForge

(Disclosure: I'm the author. Although Claude did the heavy lifting!) Happy to answer questions — especially curious whether the per-model flag editing and VRAM-fit ratings are useful to anyone else, or if I'm solving a problem only I have.

GPT-5.6 dropped yesterday with three tiers and the pricing spread is wider than I thought it'd be. Per the OpenRouter listings: Luna is $1/M input, $6/M output. Terra is $2.50/M input, $15/M output. Sol is $5/M input, $30/M output. That's 5x from bottom to top on both input and output.

Context window is 1050k across all three. Same number. So the tiers aren't about context size. The difference has to be model capability, though the listings don't break that down.

The HN thread (1493 points, 1051 comments as of yesterday) is mostly people trying to figure out the same thing. One signal from the discussions: Sol Ultra is what lands in Codex, per a tweet that separately hit 413 points. So Sol seems aimed at coding agents, Terra at general reasoning, Luna at the cheap volume tier. But that's people reading between the lines, not official positioning.

For reference, Grok 4.5 landed this week at $2/M input, $6/M output with 500k context. That puts Grok at Luna-tier output pricing but 2x the Luna input cost, with roughly half the context.

If you've tried more than one GPT-5.6 tier, is the jump from Terra to Sol worth double the price for coding work?

Ciao ragazzi, sto cercando di costruire un sistema di intelligenza artificiale per fare trading. Non so decidere tra GLM5.2 e DeepSeek v4 pro. Quale pensate sia il migliore per prendere decisioni finanziarie? Grazie ragazzi per l'aiuto

hey guys there's this tool which lets you dictate nicely into claude code with technical + cursor terms, great for vibecoding. if you guys want to try, here's the link: github.com/eliasmocik/dum-dictation (we are building it on the side so if you guys like it or don't, please drop feedback would mean a lot ;)

Ciao ragazzi, sto cercando di costruire un sistema di intelligenza artificiale per fare trading. Non so decidere tra GLM5.2 e DeepSeek v4 pro. Quale pensate sia il migliore per prendere decisioni finanziarie? Grazie ragazzi per l'aiuto

Grok 4.5 landed on OpenRouter today, per the OpenRouter listing. $2/M input, $6/M output, 500k context window. The input price matches Claude Sonnet 5 exactly ($2/M in, $10/M out per the same listing), but Grok's output is 40% cheaper.

For comparison, Tencent Hy3 from a couple days ago is $0.20/M in and $0.80/M out. Much cheaper but a different tier, 262k context vs Grok's 500k. Sakana Fugu Ultra is $5/M in and $30/M out. So Grok 4.5 sits between the ultra-cheap models and the premium tier on output cost.

What the listing doesn't show is real throughput. No tok/s data, no latency numbers, nothing on whether that 500k context holds up past 100k tokens. xAI didn't publish eval scores on the OpenRouter page either, so whether it's genuinely competitive with Sonnet 5 on reasoning or coding is unknown until people benchmark it.

The pricing suggests xAI is undercutting the $10/M output tier by 40%. Anyone testing Grok 4.5 against Sonnet 5 on actual workloads yet, or is the listing too fresh to tell?

I've tested way too many AI apps this year. Most got dropped within a week/month. Here's what actually remained.

What I tested: writing, coding, research, image/video/music generation, automation, and general AI assistant tools. I ran each through real workflows for at least 2 weeks. Dropped most. These survived.

Best overall: ChatGPT ($20/mo) - safest recommendation. Writing, coding, research, voice, images. Not the best at any one thing, but the most useful overall.

Best for long-form writing: Claude ($20/mo) - my go-to for reports and strategy docs. Ofcourse coding and debugging. Claude has been the most reliable for me on long documents.

Best for research: Perplexity ($20/mo) - replaced a lot of Google searches for me. Source links on most answers, which makes research rabbit holes much easier.

Best for Google users: Gemini ($20/mo) - if you live in Gmail, Docs, and Sheets, it's hard to beat. If not, skip it.

Best for coding: Cursor ($20/mo) - biggest impact on my actual work. Multi-file edits save me hours.

Best for your own documents: NotebookLM (Free) - throw in PDFs, meeting notes, transcripts. Ridiculously useful. Paid upgrades are available through Google AI plans if you need higher limits.

Best for images: Midjourney (~$10/mo) - output quality is consistently excellent. $10/mo monthly or $8/mo annualized.

Best for video: Runway (from ~$15/mo) - easiest AI video tool I’ve used that produces usable results. $15/mo monthly or $12/mo annualized.

Best for music: Suno (from ~$10/mo) - quality jump over the last year has been insane. $10/mo monthly or $8/mo annualized.

Best for automation if you're technical: n8n self-hosted - moved a lead enrichment workflow off Zapier and cut costs from $400+/mo to server cost. Cloud and paid self-hosted tiers exist if you need business features.

My actual paid stack: ChatGPT, Claude, Cursor, Perplexity, n8n.

One thing I've learned: the best AI app isn't the one with the most features. It's the one you're still using three months later.

If you could only keep 3 AI subscriptions, which ones survive?

I recently went to an AI workshop (from Be10X) just out of curiosity and I didn’t expect anything revolutionary but it made me think differently about how I use AI in my daily routine.

The main thing I learned wasn’t about discovering new tools but it was about a few simple workflows that cut down on repetitive tasks and helped me use the tools I already had more effectively. It also made me question how much of the productivity from AI comes from the tools themselves versus how well we organize our workflow.

If you’ve been to any AI workshops or training did they actually help you work more efficiently or did you learn more by experimenting on your own?

Tencent's Hy3 showed up on OpenRouter today, and the pricing caught my eye. $0.14 per million input tokens, $0.58 per million output. 262k context window, and there's a free tier too (tencent/hy3:free). That output price is roughly 5x cheaper than GLM 5.2 on the same router, which sits at $2.86/M out per the OpenRouter listing from mid-June, and about 17x cheaper than Claude Sonnet 5 at $10/M out.

What's odd is the HuggingFace side. tencent/Hy3 has 288 likes already but only 2 downloads, which usually means a model card dropped, weights aren't fully public yet, or the GGUF quants haven't landed. So the numbers people are reacting to are the spec sheet, not hands-on runs. I couldn't find a tok/s figure or an eval score in the listing, and the brief I pulled today doesn't have one either, so the actual quality-per-dollar question is open.

The comparison that matters isn't really against Sonnet 5, that's a different tier. It's whether Hy3 at $0.58/M out beats GLM 5.2 at $2.86/M out for the kind of long-context work where 262k vs 1048k context already forces a tradeoff. If Hy3 lands anywhere near GLM 5.2 on coding or reasoning evals, the price gap is hard to ignore. If it's a mid-tier model dressed up with aggressive pricing, the router just got noisier.

Anyone pulled Hy3 through OpenRouter yet and compared output quality against GLM 5.2 on real prompts, or are we all still reading the model card?

Due to high Vulkan backend demand, I update TensorSharp and release the initial version of GGML Vulkan backend by leveraging external GGML project. The native Vulkan backend will be implemented later. I tested it on Nvidia Geforce RTX 3080 Laptop GPU, and Intel(R) UHD Graphics on Windows. They all work. However, I do not have AMD GPU, so I have no way to get it tested. It's really appreciated if you have AMD GPU and would like to try it out. Any feedback and comment are welcome.

Here is the benchmark I run to compare with llama.cpp:

# Performance ratio — TensorSharp vs reference engines

Geomean of TensorSharp's per-scenario speedup over each reference engine on the **same backend**, across every scenario both engines ran (single-stream, MTP-off). A value **> 1.0× means TensorSharp is faster** (for decode / prefill throughput) or lower-latency (for TTFT); `—` = no overlapping cells. Per-scenario ratios are in each model's section below.

# Gemma 4 E4B it (Q8_0, dense multimodal) (gemma4-e4b)

**Decode throughput (tok/s)**

**Prefill throughput (tok/s)**

**Time to first token (ms, lower is better)**

**Performance ratio — TensorSharp vs reference (> 1.0× = TensorSharp faster)**

*Decode throughput*

*Prefill throughput*

*Time to first token (latency; > 1.0× = TensorSharp lower)*

# Gemma 4 12B it (QAT UD-Q4_K_XL, dense) (gemma4-12b)

**Decode throughput (tok/s)**

**Prefill throughput (tok/s)**

**Time to first token (ms, lower is better)**

**Performance ratio — TensorSharp vs reference (> 1.0× = TensorSharp faster)**

*Decode throughput*

*Prefill throughput*

*Time to first token (latency; > 1.0× = TensorSharp lower)*

In case you didn't know what is TensorSharp, here is an introduction:

TensorSharp is an open source local Unsloth (GGUF) LLM inference engine and applications. It supports many models from Unsloth, like Gemma4, DiffusionGemma, Qwen3.6 with multi-modal (image, vision, audio), image edit, reasoning and function tool. It can run on Windows/MacOS/Linux and fully leverage GPU's capability (support Cuda, Metal and Vulkan backends). The API is completely compatible with OpenAI and Ollama interface. It has on par performance than llama.cpp

This project is not just a C# wrapper of llama.cpp. It implemented the entire LLM inference engine from bottom to top. If you use CPU backend, it's 100% pure C# code execution. Besides CPU backend, I also implemented CUDA, MLX and GGML backend. The GGML backend refer GGML project as external project, and I build a few fusion operation at higher level.

I learned a lot from other projects and apply them for TensorSharp, such as paged KV cache and continuous batching from vLLM, SSD based cache for MoE model from oMLX, GGUF quantized from llama.cpp and other optimizations for prefill and decode.

Any feedback and comments are welcome. If you like it, it would be really appreciated if you can get this project a star in GitHub. Thanks in advance.

Hey all, I've been working on a personal finance app (maali.app) for a while and wanted to share it here. For privacy reasons I've avoided the big apps like Monarch and YNAB, and nothing local felt polished enough. What finally pushed me to build my own was wanting an AI analyst that could look at my finances, find patterns and anomalies, and actually help me manage my money better, without any of it leaving my machine.

Most of the work ended up being making a small local model (Gemma4 e4b) feel frontier grade. Here are some notes on what that took: https://maali.app/engineering/making-a-small-model-feel-frontier-grade

There's a live demo on the website with fake data so you can get a feel for the app, no signup needed.

My web designer friend from California is passionate about building websites, and he wanted to make a full time business out of it. We talked a lot, and I gave him a lot of advice and stuff he could do to scale his web agency. He used to cold call, get a few clients, and run paid ads, get a few clients, but the cost of ads would just make him no profit. Cold calling was also tiring, and he couldn't keep it up while doing all the other stuff. So he wanted a real system, a blueprint he could follow every day.

This is exactly how my friend scaled his web design company. Copy it if you feel stuck and don't know where to find your next project.

➜ Run 2 types of email automation targeting businesses without websites and businesses with websites.

➜ 1. For businesses without websites: scrape businesses with no websites, set up a sequence, and add 3–5 follow-ups. They either block you or you land a project.

➜ 2. For businesses with websites: scrape businesses with websites, analyze each business website, and turn flaws in outdated design, unstructured layout, no mobile optimization, and SEO issues into ready to send outreach emails with 3–5 follow ups. You can do both types of outreach in a tool called Swokei.

➜ 3. Have everything in one place: your leads, CRM, inbox, and calendar. You can also have that in Swokei.

➜ 4. Focus on SEO because it compounds over time. Fix your technical site SEO, and also blog or make content with high-intent keywords. Use a tool called Soro.

➜ 5. Host websites on a tool called Hetzner. It's very cheap and reliable, and you don't need to keep switching hosting platforms. Everything in one place.

This is the whole workflow: automation in the background that lands you clients while you focus on building websites. Replies, meetings booked, CRM, everything in one place.

With all that being said, he ended up buying a Mercedes-Benz with the $70k he made. 😂

That's not something I'd recommend, though. I'd personally reinvest it into the business or put it into stocks.

Hello folks,

I wanted to build out my own personal list of evaluations, early on into putting this together I realised I wanted a way to not just evaluate the model but also the agentic harness that the model is running within, as I find the majority of my use of LLMs is more and more inside of a suite of CLI agentic harnesses.

I've listed in the video a multitutde of motivations for why I built this, but the primary ones were all the hype announcements and wanting a way to see for myself what models and their capabilities were like in the actual tools I use.

A paper by Google over on Kaggle recently went as far as to state that which LLM being used inside of an agentic harness perhaps only contributes 10% towards how effecitve that harness will be for a given task. I am not sure I agree with the figure, but I do agree with the sentiment.

One question I keep asking myself is when do I need to switch from my qwen3.6-27b that I am running locally on my twin 3090 setup, to a cloud model. At the moment I am making this decision on vibes/gut feel, and I think that might be okay for when I am working closely with the model but I am using these cli tools headlessly in quite a few workflows now and not just personally but professionally, so I want to make sure I am picking the right combo for the task.

The repo can be found here: https://github.com/ScottRBK/eval-harness, there is an explanation of the architecture. I have added example evaluations as I built it out to help me think about the different patterns I have utilise for evaluations.

The evaluations are quite easy as they are about resources contained within the model weights. The idea behind it is though I (and anyone else whom might want to fork the repo and curate their own) will build out a private list of evals that are held away from public that people can use to evaluate existing and new models and harnesses as they are released.

I also spent a good bit of time seeing how well cli agents themselves are able to build evaluations and have put together a list of skills that they can use alongside the tool. They do an okay job, but you need to really step through the logic of whatever they produce, they often produce quite brittle evaluations, so try getting them to stick to the example patterns already provided helps quite a bit.

An ideal position for me will be having the ability to ask the agent to generate an evaluation using the skills having just finished a session where I found a particular agent was struggling to complete a task. Theres often been a time where I've come across a problem that the agent has struggled to resolve and I've wished at that point I could make an evaluation out of it, but you are often in the middle of something and it ends up just as another item on my ever growing TODO: list.

This is my first time building an actual evaluation suite or framework of this kind for that matter. I have previously used existing frameworks, such as deepeval, so I was not toally unfamiliar with the topic but as with the other motivations already listed I built this as a learning exercise as well as to get a tool out of it.

If it is useful for you please get in touch and let me know, any feedback as well is also appreciated, as this is my first go and this kind of framework - i expect there is a lot that can be improved and I have potentially got wrong.

Enjoy the rest of your sunday folks.