Zhipu AI

Zhipu AI AI

Zhipu AI AI

Zhipu AI AI

Zhipu AI AI

Zhipu AI AI

Zhipu AI AI

Zhipu AI AI

Zhipu AI AI

Zhipu AI AI

Zhipu AI AI

Pencil Bench (multi step reasoning benchmark)

DeepSeek was a scam from the beginning submitted by /u/DigSignificant1419 [link] [comments]

I got tired of 3 AM PagerDuty alerts, so I built an AI agent to fix cloud outages while I sleep. (Built with GLM-5.1)

If you've ever been on-call, you know the nightmare. It’s 3:15 AM. You get pinged because heavily-loaded database nodes in us-east-1 are randomly dropping packets. You groggily open your laptop, ssh into servers, stare at Grafana charts, and manually reroute traffic to the European fallback cluster. By the time you fix it, you've lost an hour of sleep, and the company has lost a solid chunk of change in downtime. This weekend for the Z.ai hackathon, I wanted to see if I could automate this specific pain away. Not just "anomaly detection" that sends an alert, but an actual agent that analyzes the failure, proposes a structural fix, and executes it. I ended up building Vyuha AI-a triple-cloud (AWS, Azure, GCP) autonomous recovery orchestrator. Here is how the architecture actually works under the hood. The Stack I built this using Python (FastAPI) for the control plane, Next.js for the dashboard, a custom dynamic reverse proxy, and GLM-5.1 doing the heavy lifting for the reasoning engine. The Problem with 99% of "AI DevOps" Tools Most AI monitoring tools just ingest logs and summarize them into a Slack message. That’s useless when your infrastructure is actively burning. I needed an agent with long-horizon reasoning. It needed to understand the difference between a total node crash (DEAD) and a node that is just acting weird (FLAKY or dropping 25% of packets). How Vyuha Works (The Triaging Loop) I set up three mock cloud environments (AWS, Azure, GCP) behind a dynamic FastApi proxy. A background monitor loop probes them every 5 seconds. I built a "Chaos Lab" into the dashboard so I could inject failures on demand. Here’s what happens when I hard-kill the GCP node: Detection: The monitor catches the 503 Service Unavailable or timeout in the polling cycle. Context Gathering: It doesn't instantly act. It gathers the current "formation" of the proxy, checks response times of the surviving nodes, and bundles that context. Reasoning (GLM-5.1): This is where I relied heavily on GLM-5.1. Using ZhipuAI's API, the agent is prompted to act as a senior SRE. It parses the failure, assesses the severity, and figures out how to rebalance traffic without overloading the remaining nodes. The Proposal: It generates a strict JSON payload with reasoning, severity, and the literal API command required to reroute the proxy. No Rogue AI (Human-in-the-Loop) I don't trust LLMs enough to blindly let them modify production networking tables, obviously. So the agent operates on a strict Human-in-the-Loop philosophy. The GLM-5.1 model proposes the fix, explains why it chose it, and surfaces it to the dashboard. The human clicks "Approve," and the orchestrator applies the new proxy formation. Evolutionary Memory (The Coolest Feature) This was my favorite part of the build. Every time an incident happens, the system learns. If the human approves the GLM's failover proposal, the agent runs a separate "Reflection Phase." It analyzes what broke and what fixed it, and writes an entry into a local SQLite database acting as an "Evolutionary Memory Log". The next time a failure happens, the orchestrator pulls relevant past incidents from SQLite and feeds them into the GLM-5.1 prompt. The AI literally reads its own history before diagnosing new problems so it doesn't make the same mistake twice. The Struggles It wasn't smooth. I lost about 4 hours to a completely silent Pydantic validation bug because my frontend chaos buttons were passing the string "dead" but my backend Enums strictly expected "DEAD". The agent just sat there doing nothing. LLMs are smart, but type-safety mismatches across the stack will still humble you. Try it out I built this to prove that the future of SRE isn't just better dashboards; it's autonomous, agentic infrastructure. I’m hosting it live on Render/Vercel. Try hitting the "Hard Kill" button on GCP and watch the AI react in real time. Would love brutal feedback from any actual SREs or DevOps engineers here. What edge case would break this in a real datacenter? submitted by /u/Evil_god7 [link] [comments]

Need Help PLeech

So i started using claude code with GLM API ffrom Zhipu ai. and whenever i request changes for example in the UI, it does it but i dont see it on the actual website like the changes arent literally made. Ive deployed it to vercel too btw. Also is it normal for each tasks or simple questions to take an unusally long time for a response? submitted by /u/justaleafhere [link] [comments]

Artificial Analysis Intelligence Index and cost benchmarks are useful decision/guidance determinants for which models to use. Analysis for top models.

# AI Intelligence and Benchmarking Cost (Feb 2026) As per the **Artificial Analysis Intelligence Index v4.0** (February 2026), the scoring ceiling is set by **Claude Opus 4.6 (max) at 53**. ## Adjusted Score Formula The "Adjusted Score" follows a quadratic penalty formula: ``` Adjusted Score = 53 × (1 - (53 - Intel Score)² / 53²) ``` This creates a steeper penalty for performance gaps compared to a linear scale. ## Model Comparison Table | Lab | Model | Intel Score | Adjusted Score | Benchmark Cost | Intel Ratio (Score/Cost) | Adj. Ratio (Adj/Cost) | |-----------|-------|-------------|----------------|----------------|--------------------------|----------------------| | Anthropic | Claude Opus 4.6 (max) | 53 | 53 | $2,486.45 | 0.021 | 0.021 | | OpenAI | GPT-5.2 (xhigh) | 51 | 49 | $2,304.00* | 0.022 | 0.021 | | Zhipu AI | GLM-5 (Reasoning) | 50 | 47 | $384.00* | 0.130 | 0.122 | | Google | Gemini 3 Pro | 48 | 43 | $1,179.00* | 0.041 | 0.036 | | MiniMax | MiniMax-M2.5 | 42 | 31 | $124.58 | 0.337 | 0.249 | | DeepSeek | DeepSeek V3.2 (Reasoning) | 42 | 31 | $70.64 | 0.595 | 0.439 | | xAI | Grok 4 (Reasoning) | 41 | 29 | $1,568.34 | 0.026 | 0.018 | *\*Benchmark costs for proprietary models are based on Artificial Analysis evaluation token counts (typically 12M–88M depending on verbosity) multiplied by current API rates.* ## Key Insights 1. **High token reasoning models**: Grok 4 and Claude Opus 4.6 use a high number of tokens during reasoning, up to **88M tokens**. This results in low Intel-to-Cost ratios despite high scores. 2. **DeepSeek V3.2 is the most efficient**: It provides an adjusted intelligence ratio that is roughly **20 times better** than the proprietary frontier. 3. **Cost efficiency comparison**: MiniMax-M2.5 and DeepSeek V3.2 share a score of 42. DeepSeek is almost **twice as cost-effective** due to lower API pricing and higher token efficiency. ## Visual Summary ``` Intel Score vs Cost Efficiency (Adjusted Ratio) ───────────────────────────────────────────────── DeepSeek V3.2 ████████████████████████████ 0.439 MiniMax-M2.5 ███████████████ 0.249 GLM-5 ███████ 0.122 Gemini 3 Pro ██ 0.036 Claude Opus 4.6 █ 0.021 GPT-5.2 █ 0.021 Grok 4 █ 0.018 ``` --- *Source: Artificial Analysis Intelligence Index v4.0, February 2026* google AI mode made analysis, GLM 5 formatted and added cute graph. this combines the intelligence score and cost to run the intelligence benchmark from https://artificialanalysis.ai/?endpoints=openai_gpt-5-2-codex%2Cazure_kimi-k2-thinking%2Camazon-bedrock_qwen3-coder-480b-a35b-instruct%2Camazon-bedrock_qwen3-coder-30b-a3b-instruct%2Ctogetherai_minimax-m2-5_fp4%2Ctogetherai_glm-5_fp4%2Ctogetherai_qwen3-next-80b-a3b-reasoning%2Cgoogle_gemini-3-pro_ai-studio%2Cgoogle_glm-4-7%2Cmoonshot-ai_kimi-k2-thinking_turbo%2Cnovita_glm-5_fp8 look at intelligence vs cost graph for further insight. You can add much smaller models for comparison to LLMs you might run locally. The adjusted intelligence/cost metric is a useful heuristic for "how much would you pay extra to get top score". Choosing non-open models requires a much higher penalty than 2x the difference/comparison to highest score. Quantized versions don't seem to score lower. This site provides good base info to make your own model of "score deficit", model size, tps as a combined score relative to tokens/cost to get a benchmark score. I was originally researching how grok 4.2 approach would inflate costs vs performance, but it is not yet benchmarked.