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I think we're reaching a point where context length, reasoning quality, and cost matter more than another leaderboard screenshot. After spending some time with Tencent Hunyuan Hy3, that's exactly the impression I got. The model handled long coding sessions, instruction following, and agent-style tasks without feeling unnecessarily "heavy." Instead...

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12,378 просмотров • 2 месяцев назад •via X (Twitter)

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Not every new model needs to be the biggest model. Sometimes I'd rather have one that's fast, reliable, affordable, and actually pleasant to build with. That's the impression I got after using Tencent Hunyuan Hy3. I threw a mix of coding, reasoning, and agent tasks at it throughout the afternoon, and what stood out was how stable the experience felt. It followed instructions well, kept track of long conversations, and didn't require much prompt babysitting. The 256K context window is especially useful when working with larger projects, and Tencent says Hy3 reaches performance comparable to flagship open-source models 2–5× larger in parameter scale, which is an interesting trade-off between capability and efficiency. Being Apache 2.0 open source is another bonus, and it's currently free to try on OpenRouter for the next two weeks. Worth taking for a spin if you're evaluating models for real-world development. 👉 Thanks Tencent Hy #Hy3 #Hunyuan #OpenSource #TencentAI
0:22

Not every new model needs to be the biggest model. Sometimes I'd rather have one that's fast, reliable, affordable, and actually pleasant to build with. That's the impression I got after using Tencent Hunyuan Hy3. I threw a mix of coding, reasoning, and agent tasks at it throughout the afternoon, and what stood out was how stable the experience felt. It followed instructions well, kept track of long conversations, and didn't require much prompt babysitting. The 256K context window is especially useful when working with larger projects, and Tencent says Hy3 reaches performance comparable to flagship open-source models 2–5× larger in parameter scale, which is an interesting trade-off between capability and efficiency. Being Apache 2.0 open source is another bonus, and it's currently free to try on OpenRouter for the next two weeks. Worth taking for a spin if you're evaluating models for real-world development. 👉 Thanks Tencent Hy #Hy3 #Hunyuan #OpenSource #TencentAI

FELIX

56,379 просмотров • 2 месяцев назад

Tencent's Hy3 lands today with real coding and agent ability and a price that undercuts the models you'd put it against. It's a 295B MoE with only 21B active parameters, a big part of why it stays cheap to serve, plus a 256K context window and an Apache 2.0 license that even outperforms GLM-5.1 although GLM is 2.5x larger. On OpenRouter it runs at $0.14 per million input tokens and $0.58 per million output. For a model Tencent benchmarks against much larger flagships, that pricing is insane. Price-performance-ratio is China's real moat. I already wrote about Tencent Hy3 earlier and the significant leap in quality it represents. Therefore, I'm pleased to recommend this model from Tencent Hy. Open Router link down below: #Hy3 #Hunyuan #AICoding
0:45

Tencent's Hy3 lands today with real coding and agent ability and a price that undercuts the models you'd put it against. It's a 295B MoE with only 21B active parameters, a big part of why it stays cheap to serve, plus a 256K context window and an Apache 2.0 license that even outperforms GLM-5.1 although GLM is 2.5x larger. On OpenRouter it runs at $0.14 per million input tokens and $0.58 per million output. For a model Tencent benchmarks against much larger flagships, that pricing is insane. Price-performance-ratio is China's real moat. I already wrote about Tencent Hy3 earlier and the significant leap in quality it represents. Therefore, I'm pleased to recommend this model from Tencent Hy. Open Router link down below: #Hy3 #Hunyuan #AICoding

Chubby♨️

23,439 просмотров • 2 месяцев назад

Tencent just dropped Hy3, and it's worth a look if you're building AI agents. I spent some time putting it through its paces today. A quick rundown of what makes this release notable: → 295B total parameters (Mixture-of-Experts), but only 21B active at inference — a real efficiency play → 256K context window for handling large codebases or long documents → Purpose-built improvements for coding and multi-step agent tasks → Fully open under Apache 2.0 — no restrictive commercial terms → A free two-week API window currently live on OpenRouter On the practical side: prompts involving layered instructions and coding tasks came back fast and coherent. Tencent says this release builds directly on feedback from 50+ internal product teams following an earlier preview, with reported drops in hallucination rate (12.5% → 5.4%) and commonsense errors (25.4% → 12.7%). Those are Tencent's own numbers, so treat them as a starting point rather than gospel until third parties weigh in. For context: Tencent's own comparisons put Hy3 behind GLM-5.2 specifically on coding benchmarks — GLM is a much bigger model (~744B total), so the trade-off makes sense. Hy3's pitch isn't "biggest," it's "efficient enough to actually deploy." Bottom line — if you're evaluating open-weight options for agent workloads, this is a solid one to add to the testing queue while the free window is open. Try it here: Tencent Hy #Hy3 #Hunyuan #TencentAI #AICoding
0:43

Tencent just dropped Hy3, and it's worth a look if you're building AI agents. I spent some time putting it through its paces today. A quick rundown of what makes this release notable: → 295B total parameters (Mixture-of-Experts), but only 21B active at inference — a real efficiency play → 256K context window for handling large codebases or long documents → Purpose-built improvements for coding and multi-step agent tasks → Fully open under Apache 2.0 — no restrictive commercial terms → A free two-week API window currently live on OpenRouter On the practical side: prompts involving layered instructions and coding tasks came back fast and coherent. Tencent says this release builds directly on feedback from 50+ internal product teams following an earlier preview, with reported drops in hallucination rate (12.5% → 5.4%) and commonsense errors (25.4% → 12.7%). Those are Tencent's own numbers, so treat them as a starting point rather than gospel until third parties weigh in. For context: Tencent's own comparisons put Hy3 behind GLM-5.2 specifically on coding benchmarks — GLM is a much bigger model (~744B total), so the trade-off makes sense. Hy3's pitch isn't "biggest," it's "efficient enough to actually deploy." Bottom line — if you're evaluating open-weight options for agent workloads, this is a solid one to add to the testing queue while the free window is open. Try it here: Tencent Hy #Hy3 #Hunyuan #TencentAI #AICoding

Felix

36,925 просмотров • 2 месяцев назад

1/ Tencent releases HY World 1.5 (WorldPlay), an open-source real-time world model with 24fps A streaming video diffusion model that enables real-time, interactive world modeling with long-term geometric consistency In direct comparison to Genie 3, it's technically inferior, and there's no information on how long the consistency will last. However, Tencent has released a significant world model as open source for the first time, making it available to everyone. A few more examples, a thread 🧵
1:06

1/ Tencent releases HY World 1.5 (WorldPlay), an open-source real-time world model with 24fps A streaming video diffusion model that enables real-time, interactive world modeling with long-term geometric consistency In direct comparison to Genie 3, it's technically inferior, and there's no information on how long the consistency will last. However, Tencent has released a significant world model as open source for the first time, making it available to everyone. A few more examples, a thread 🧵

Chubby♨️

37,776 просмотров • 8 месяцев назад

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI
1:27

I gave an AI one instruction: “Build me a playable 3D game from scratch.” Then I let it cook. 👀 I tested Tencent Hy4 preview inside WorkBuddy, and it went way beyond just generating a few snippets of code. It could: → Plan the game structure → Generate the code across files → Create the UI and interactions → Debug problems along the way → Iterate on the project while keeping context That last part is what caught my attention. Hy4 preview is built for these long-horizon tasks, with 770B total parameters, 49B active parameters, and a 1M-token context window. It’s designed for coding agents, game development, tool use, research, and other workflows where the model has to actually do the work, not just answer a prompt. And right now, you can try Hy4 preview for free on WorkBuddy until September 10. If you want to see how far an AI agent can take a real project, try it yourself: See what you can build with it. Tencent AI

Toha Khan

66,150 просмотров • 7 дней назад

The next big advantage in AI might not come from one breakthrough. It might come from how quickly a team can build, learn, and improve. Tencent Hunyuan has now released three major models in six months. Hy4 preview is the latest result of that pace. It is fully open source, with 770B total parameters, 49B active parameters, and support for more than 1M tokens of context. The model is designed around practical work, including coding, office productivity, science, planning, and longer workflows that require sustained execution. Access is priced at $0.834 per million input tokens, $2.501 per million output tokens, and $0.042 per million cache hits. That combination of rapid iteration, open access, and affordable usage is what makes this release worth paying attention to. Hy4 preview can be experienced free on WorkBuddy for the next two weeks. Tencent Hy Tencent AI WorkBuddy
0:39

The next big advantage in AI might not come from one breakthrough. It might come from how quickly a team can build, learn, and improve. Tencent Hunyuan has now released three major models in six months. Hy4 preview is the latest result of that pace. It is fully open source, with 770B total parameters, 49B active parameters, and support for more than 1M tokens of context. The model is designed around practical work, including coding, office productivity, science, planning, and longer workflows that require sustained execution. Access is priced at $0.834 per million input tokens, $2.501 per million output tokens, and $0.042 per million cache hits. That combination of rapid iteration, open access, and affordable usage is what makes this release worth paying attention to. Hy4 preview can be experienced free on WorkBuddy for the next two weeks. Tencent Hy Tencent AI WorkBuddy

Manish Kumar Shah

42,263 просмотров • 12 дней назад

🚨 I just built a game with an open-source AI model. And honestly… I didn’t expect it to be this capable. Tencent Hunyuan just released Hy4 preview, and it’s already pushing into the top tier of open-source models. Three major releases in six months. That pace is crazy. Here’s what Hy4 preview brings: → 770B total parameters → 49B active parameters → 1M+ token context window → Fully open-source But the numbers aren’t even the most interesting part. Hy4 preview was built around one goal: real-world productivity. Coding. Engineering. Office work. Science. Gaming. Finance. Security. And Tencent didn’t build it in isolation. Hy4 preview was co-designed alongside real products like WorkBuddy, using expertise and real-world data from across Tencent’s ecosystem. So I decided to test it the way I actually like testing AI models: I gave it a game idea and let WorkBuddy help turn it into a playable experience. 🎮 From the initial concept to the actual game logic, it was surprisingly smooth. And the benchmark results back up the hype: 163 internal experts 203 engineering tasks Hy4 preview — 2.99/4 Kimi K3 — 2.94/4 GLM 5.3 — 2.92/4 It also beats GLM 5.2 on benchmarks and comes remarkably close to GLM 5.3. Then comes the part I really like: 💰 ¥6/M input tokens 💰 ¥18/M output tokens 💰 ¥0.30/M cache hits Flagship-level capability without the flagship-level price. And right now, you can try Hy4 preview FREE through WorkBuddy for the next two weeks. If you’re curious what it can actually do, don’t just read the benchmarks. Build something with it. 🔗 Tencent Hy Tencent AI
0:30

🚨 I just built a game with an open-source AI model. And honestly… I didn’t expect it to be this capable. Tencent Hunyuan just released Hy4 preview, and it’s already pushing into the top tier of open-source models. Three major releases in six months. That pace is crazy. Here’s what Hy4 preview brings: → 770B total parameters → 49B active parameters → 1M+ token context window → Fully open-source But the numbers aren’t even the most interesting part. Hy4 preview was built around one goal: real-world productivity. Coding. Engineering. Office work. Science. Gaming. Finance. Security. And Tencent didn’t build it in isolation. Hy4 preview was co-designed alongside real products like WorkBuddy, using expertise and real-world data from across Tencent’s ecosystem. So I decided to test it the way I actually like testing AI models: I gave it a game idea and let WorkBuddy help turn it into a playable experience. 🎮 From the initial concept to the actual game logic, it was surprisingly smooth. And the benchmark results back up the hype: 163 internal experts 203 engineering tasks Hy4 preview — 2.99/4 Kimi K3 — 2.94/4 GLM 5.3 — 2.92/4 It also beats GLM 5.2 on benchmarks and comes remarkably close to GLM 5.3. Then comes the part I really like: 💰 ¥6/M input tokens 💰 ¥18/M output tokens 💰 ¥0.30/M cache hits Flagship-level capability without the flagship-level price. And right now, you can try Hy4 preview FREE through WorkBuddy for the next two weeks. If you’re curious what it can actually do, don’t just read the benchmarks. Build something with it. 🔗 Tencent Hy Tencent AI

Aryan Rakib

62,089 просмотров • 1 день назад

🚨 This Chinese model is INSANE !! I just tested Tencent's Hunyuan AI model to build a professional landing page & the output is mind blowing 🤯 Here's my exact prompt: "Create a modern landing page for a crypto related product with beautiful hero section, 3d elements, pricing table, and a whitelist form" What it generated: → Clean, responsive front-end code → Complex UI components with smooth interactions → Built-in debugging (it fixed its own errors) → Production-ready in minutes This 295B parameter model has 2 insane agents: → Coding Agent (handles the entire front-end) → Working Agent (task planning & multi-tool workflows) It also has a context window of 256k tokens !! Watch the video to see the full build 🔥 Try it: Tencent Hy
0:48

🚨 This Chinese model is INSANE !! I just tested Tencent's Hunyuan AI model to build a professional landing page & the output is mind blowing 🤯 Here's my exact prompt: "Create a modern landing page for a crypto related product with beautiful hero section, 3d elements, pricing table, and a whitelist form" What it generated: → Clean, responsive front-end code → Complex UI components with smooth interactions → Built-in debugging (it fixed its own errors) → Production-ready in minutes This 295B parameter model has 2 insane agents: → Coding Agent (handles the entire front-end) → Working Agent (task planning & multi-tool workflows) It also has a context window of 256k tokens !! Watch the video to see the full build 🔥 Try it: Tencent Hy

AGAFE

110,528 просмотров • 2 месяцев назад

Jensen says Open Source can be MORE expensive for enterprises, Brad Gerstner explains why: Brad Gerstner: “Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, etc. And Elon comes out and says, ‘Not so fast. We're entering the singularity, and the frontier models are way further ahead than people think.’ I believe that to be true. And then Jensen came out this week and said, ‘Closed models are actually cheaper if you don't have to build it for yourself, the training costs, and a lot of expertise to fine-tune, and maintain, and guardrail, and keep it safe. So he's basically making the argument that not only are the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to be, which I think explains why (closed models) continue to run away with it on the revenue side of the equation. For the vast majority of use cases, I think token consumption is going up for the open source guys, while share of economics is going up for the frontier labs. I think that's what we want to see.” $NVDA
0:57

Jensen says Open Source can be MORE expensive for enterprises, Brad Gerstner explains why: Brad Gerstner: “Over the last two weeks, everybody's been saying that the Chinese have caught up, that open source tokens have caught up in intelligence, that they're much cheaper, etc. And Elon comes out and says, ‘Not so fast. We're entering the singularity, and the frontier models are way further ahead than people think.’ I believe that to be true. And then Jensen came out this week and said, ‘Closed models are actually cheaper if you don't have to build it for yourself, the training costs, and a lot of expertise to fine-tune, and maintain, and guardrail, and keep it safe. So he's basically making the argument that not only are the frontier models further ahead, but that the cost differential between the two is not what everybody's making it out to be, which I think explains why (closed models) continue to run away with it on the revenue side of the equation. For the vast majority of use cases, I think token consumption is going up for the open source guys, while share of economics is going up for the frontier labs. I think that's what we want to see.” $NVDA

The All-In Podcast

61,928 просмотров • 1 месяц назад

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:
0:31

I have been testing DeepSeek-V4-Pro with the Pi coding agent. I am mindblown by how well it works out of the box. A few notes: I spent a few hours building an LLM wiki with an agent powered entirely by DeepSeek-V4-Pro on Fireworks inference. This is the first time I feel like there is an open-weight model that can reason at the level of Claude and Codex. And it does this in a cost-effective way with support for 1M context length. To be clear, I am using DeepSeek-V4-Pro inside of Pi without any special configuration. It works out of the box. It's exciting that there is a model that can just be plugged into a basic harness like Pi, and it just works. I've never seen that before. Most models require lots of configuration and setup. DeepSeek's DeepSeek-V4-Pro is clearly good at agentic coding (probably the best from the open-weight models), but the model is also great on knowledge-intensive tasks where reasoning matters. The agent pulled agentic engineering best practices from different company docs (Anthropic, OpenAI, Google, Stripe, Meta, Modal, DeepSeek, Mistral, Cohere), searched and digested Reddit and HN threads, summarized arxiv papers, and surfaced trending GitHub repos. Then it distilled everything into actionable tips across categories. I love the Wiki it built. The quality is really good. Here is a snapshot of what the wiki looks like: DeepSeek-V4-Pro handled the task without breaking stride. Multi-step research queries, code generation for scaffolding, context-heavy reasoning across disparate sources. For coding specifically, this is the first open-weight model that genuinely feels like a Codex or Claude Code experience. It compares in capability and actual multi-turn agentic work. What made the loop feel so responsive was Fireworks' inference speed (the fastest in the market) and the fact that they actually validate models at the systems level before shipping. No corrupted reasoning traces. Just fast, reliable iteration. The hybrid CSA and HCA attention design cuts KV cache to just 10% and inference FLOPs by nearly 4x at 1M-token context. This is what makes the agent loop actually fast and cheap enough to run in practice. For devs who've been watching open-weight models close the gap but haven't found one that actually delivers in practice, this is the closest I've seen. Try it here:

elvis

60,091 просмотров • 4 месяцев назад

🚨 Tencent just dropped a serious open-source AI contender. Meet Hy4 Preview built for productivity, coding & agentic workflows. 770B total parameters. 49B active. 1M+ token context. But the numbers aren’t the most interesting part. Hy4 Preview is designed to handle what actually matters: planning, tool use, coding, research, document workflows and complex multi-step tasks. In Tencent’s internal blind evaluation across 203 engineering tasks, it scored 2.99/4, edging out GLM 5.3 (2.92) and Kimi K3 (2.94). From full-stack development and research to workplace automation and even game development, this looks less like “just another LLM” and more like a model built to get real work done. Worth watching 👀 🔗 Try it with WorkBuddy: Tencent Hy Tencent AI WorkBuddy
1:02

🚨 Tencent just dropped a serious open-source AI contender. Meet Hy4 Preview built for productivity, coding & agentic workflows. 770B total parameters. 49B active. 1M+ token context. But the numbers aren’t the most interesting part. Hy4 Preview is designed to handle what actually matters: planning, tool use, coding, research, document workflows and complex multi-step tasks. In Tencent’s internal blind evaluation across 203 engineering tasks, it scored 2.99/4, edging out GLM 5.3 (2.92) and Kimi K3 (2.94). From full-stack development and research to workplace automation and even game development, this looks less like “just another LLM” and more like a model built to get real work done. Worth watching 👀 🔗 Try it with WorkBuddy: Tencent Hy Tencent AI WorkBuddy

RAVI KUMAR SAHU

63,568 просмотров • 13 дней назад

I got to try Grok 4.5 in early access in Cursor for the past few days and I absolutely enjoyed it. It feels like Opus 4.8 at 2x the speed at a much cheaper price point. I tasked it to brainstorm > plan > implement a big feature for my game (this act 1 boss fight) and it did not disappoint. - It is much smarter than Composer 2.5, during planning mode, it is able to think through my request more robustly, ensuring that edge cases are covered and makes sure to ask the right questions to confirm with me first. - It is much better at brainstorming ideas/suggestions, similar to Opus 4.8, though I think Fable still edges out a little when it comes to brainstorming ideas and suggestions - It is FAST. probably the fastest of all frontier models (Opus 4.8, GPT 5.5 etc), which makes it a joy to build with, because I can stay in the flow - It has much improved visual/animation capabilities than Composer 2.5, it can code up animations (i wanted an explosion animation with particle effects) with much, much better visuals, animation movement and timing. This is a big leap and I was so happy to see this improvement. - The best part for me is that I can just use the same model from planning down to execution without switching to a lower cost model because the price point is cheaper than other frontier models. I'll be testing this model with more challenging tasks in the next few days but I think this is going to be my main driver for vibe coding for a while. Also, its nice to see Grok back in the race. 🙌
0:34

I got to try Grok 4.5 in early access in Cursor for the past few days and I absolutely enjoyed it. It feels like Opus 4.8 at 2x the speed at a much cheaper price point. I tasked it to brainstorm > plan > implement a big feature for my game (this act 1 boss fight) and it did not disappoint. - It is much smarter than Composer 2.5, during planning mode, it is able to think through my request more robustly, ensuring that edge cases are covered and makes sure to ask the right questions to confirm with me first. - It is much better at brainstorming ideas/suggestions, similar to Opus 4.8, though I think Fable still edges out a little when it comes to brainstorming ideas and suggestions - It is FAST. probably the fastest of all frontier models (Opus 4.8, GPT 5.5 etc), which makes it a joy to build with, because I can stay in the flow - It has much improved visual/animation capabilities than Composer 2.5, it can code up animations (i wanted an explosion animation with particle effects) with much, much better visuals, animation movement and timing. This is a big leap and I was so happy to see this improvement. - The best part for me is that I can just use the same model from planning down to execution without switching to a lower cost model because the price point is cheaper than other frontier models. I'll be testing this model with more challenging tasks in the next few days but I think this is going to be my main driver for vibe coding for a while. Also, its nice to see Grok back in the race. 🙌

Danny Limanseta

1,421,893 просмотров • 2 месяцев назад

Nothing beats open-source! We now have a fully open-source coding agent that can compete with any proprietary solution out there: • 500+ available models • It doesn't train on your code • It doesn't charge a penny for tokens • It's open. You can change if you want. Kilo Code is the #1 app on OpenRouter right now, and the community is releasing new features on GitHub at rocket speed (about 20 releases in the last 30 days). I installed it in Visual Studio Code, but you can install it in pretty much any IDE. I need to spend more time with it, but here is a very interesting design choice: Most coding agents support two modes: an "agentic" mode for writing code and an "ask" mode for talking to the model without making any changes. Kilo Code supports 5 different modes: 1. Architect mode - Research, design, and planning 2. Debug mode - Problem solving and troubleshooting 3. Code mode - To write code. 4. Ask mode - To talk to the model. 5. Orchestrator mode - Combine every agent to solve problems. On paper, I really like this. I'll need more time to try out the specialized agents and see whether niching becomes an advantage. Here is the link to the website: Thanks to the Kilo for partnering with me on this post.
6:17

Nothing beats open-source! We now have a fully open-source coding agent that can compete with any proprietary solution out there: • 500+ available models • It doesn't train on your code • It doesn't charge a penny for tokens • It's open. You can change if you want. Kilo Code is the #1 app on OpenRouter right now, and the community is releasing new features on GitHub at rocket speed (about 20 releases in the last 30 days). I installed it in Visual Studio Code, but you can install it in pretty much any IDE. I need to spend more time with it, but here is a very interesting design choice: Most coding agents support two modes: an "agentic" mode for writing code and an "ask" mode for talking to the model without making any changes. Kilo Code supports 5 different modes: 1. Architect mode - Research, design, and planning 2. Debug mode - Problem solving and troubleshooting 3. Code mode - To write code. 4. Ask mode - To talk to the model. 5. Orchestrator mode - Combine every agent to solve problems. On paper, I really like this. I'll need more time to try out the specialized agents and see whether niching becomes an advantage. Here is the link to the website: Thanks to the Kilo for partnering with me on this post.

Santiago

73,062 просмотров • 10 месяцев назад

Tried Hy4 preview through WorkBuddy and gave it a pretty vague prompt for a first person roller coaster ride, front row seat, make it feel fast, that was basically it. No detailed spec, no reference video, just a rough idea in a sentence. What came back actually held up. Steep drop that felt like the bottom dropping out, banked turns that leaned the right way, scenery rushing by close enough that you register the speed instead of just watching it happen. For something built off a loose prompt like that, I did not expect it to nail the feel this well. Coding and 3D rendering have clearly gotten a real upgrade here, and this took one pass to look this good. Free to try in WorkBuddy for the next two weeks if you want to mess with it yourself. Tencent Hy Tencent AI
1:27

Tried Hy4 preview through WorkBuddy and gave it a pretty vague prompt for a first person roller coaster ride, front row seat, make it feel fast, that was basically it. No detailed spec, no reference video, just a rough idea in a sentence. What came back actually held up. Steep drop that felt like the bottom dropping out, banked turns that leaned the right way, scenery rushing by close enough that you register the speed instead of just watching it happen. For something built off a loose prompt like that, I did not expect it to nail the feel this well. Coding and 3D rendering have clearly gotten a real upgrade here, and this took one pass to look this good. Free to try in WorkBuddy for the next two weeks if you want to mess with it yourself. Tencent Hy Tencent AI

Chubby♨️

27,793 просмотров • 13 дней назад

Tencent just dropped Hy4 preview, and it's officially open source now. 770B total params, 49B active, 1M+ token context. three major releases in six months, and this one just landed in the top tier of open source models. → Built through Tencent's co-design loop: training data shaped with experts across software engineering, gaming, finance, and security, then built directly alongside products like WorkBuddy → In a blind test with 163 internal experts across 203 engineering tasks, it scored 2.99/4, ahead of GLM 5.3 at 2.92 and Kimi K3 at 2.94 → Benchmarks put it clearly past GLM 5.2 and neck and neck with GLM 5.3 → Pricing stayed low: $0.834/M input, $2.501/M output, $0.042/M cache hits I asked it to write a weekly status update email template for a team lead, then generate a filled-out example with placeholder projects, and export both as Word docs. one prompt, no follow-up, and it handed back both files clean and ready to send. free to try for the next two weeks through WorkBuddy: Tencent Hy Tencent AI WorkBuddy
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Tencent just dropped Hy4 preview, and it's officially open source now. 770B total params, 49B active, 1M+ token context. three major releases in six months, and this one just landed in the top tier of open source models. → Built through Tencent's co-design loop: training data shaped with experts across software engineering, gaming, finance, and security, then built directly alongside products like WorkBuddy → In a blind test with 163 internal experts across 203 engineering tasks, it scored 2.99/4, ahead of GLM 5.3 at 2.92 and Kimi K3 at 2.94 → Benchmarks put it clearly past GLM 5.2 and neck and neck with GLM 5.3 → Pricing stayed low: $0.834/M input, $2.501/M output, $0.042/M cache hits I asked it to write a weekly status update email template for a team lead, then generate a filled-out example with placeholder projects, and export both as Word docs. one prompt, no follow-up, and it handed back both files clean and ready to send. free to try for the next two weeks through WorkBuddy: Tencent Hy Tencent AI WorkBuddy

D-Coder

108,522 просмотров • 12 дней назад

This is next-level smart: An open-source platform that evaluates your prompts and automatically refines them based on the results. ​ Of course, it feels obvious after you see it: ​ • You write a prompt • The system evaluates it across different scenarios • Based on the results, it refines it to improve results ​ I recorded a quick video to show you how it works. It's pretty cool stuff! ​ Here are some of the problems and best practices for teams building AI applications: ​ 1. Testing your prompts manually doesn't scale 2. Prompts should not be spread throughout the codebase 3. Non-technical people need easy access to your prompts 4. Prompts can always use a version history to track changes 5. Monitoring the performance of prompts overtime is critical ​ Evaluating the prompts is what keeps me up at night from this list. Of all the conversations I've had with companies and people building AI applications, this is the area that's causing the most pain. ​ Testing a prompt is difficult. Think about how you'd test the response of a model subjectively. What do you account for, "tone," "objectivity," "completeness," "creativity," "readability," etc.? ​ Last week, I met the developers behind Latitude, an open-source prompt engineering platform trying to solve all of these issues. You can try the platform in two ways: ​ • You can self-host the platform. Free and open-source. • If you want to try their online product, their free tier is huge. ​ Here is the link: ​ Thanks to the Latitude team for collaborating with me on this post, and congratulations on going live with their product!
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This is next-level smart: An open-source platform that evaluates your prompts and automatically refines them based on the results. ​ Of course, it feels obvious after you see it: ​ • You write a prompt • The system evaluates it across different scenarios • Based on the results, it refines it to improve results ​ I recorded a quick video to show you how it works. It's pretty cool stuff! ​ Here are some of the problems and best practices for teams building AI applications: ​ 1. Testing your prompts manually doesn't scale 2. Prompts should not be spread throughout the codebase 3. Non-technical people need easy access to your prompts 4. Prompts can always use a version history to track changes 5. Monitoring the performance of prompts overtime is critical ​ Evaluating the prompts is what keeps me up at night from this list. Of all the conversations I've had with companies and people building AI applications, this is the area that's causing the most pain. ​ Testing a prompt is difficult. Think about how you'd test the response of a model subjectively. What do you account for, "tone," "objectivity," "completeness," "creativity," "readability," etc.? ​ Last week, I met the developers behind Latitude, an open-source prompt engineering platform trying to solve all of these issues. You can try the platform in two ways: ​ • You can self-host the platform. Free and open-source. • If you want to try their online product, their free tier is huge. ​ Here is the link: ​ Thanks to the Latitude team for collaborating with me on this post, and congratulations on going live with their product!

Santiago

64,157 просмотров • 1 год назад

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇
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CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

Md Riyazuddin

31,199 просмотров • 2 месяцев назад