Syrin AI

@askOkara syrin.ai

drop your website and i'll tell you which marketing channel to focus on first

@launch_llama syrin.ai 💪💪

Drop your startup below. I read every single one. The best get featured to 45k founders in Launch Llama 👇 #buildinpublic

@NaivaidyaY66600 syrin.ai

Hey Founders. What are you building this week? Drop your products link below 👇

@upen946 syrin.ai

✋✋ Monday again!! Time to promote your product. 🚀 Share your product URL

@ray_civ 100%. Would love for you to try this feature out at syrin.ai

@syrinlabs This is the right direction. Production A/B testing beats offline opinion every time, as long as the guardrails are strong enough to keep bad variants from shipping.

We built A/B testing for AI agents. Not offline evals. Not test datasets. Actual production traffic split between config variants. Prompt A vs Prompt B. GPT-4o vs GPT-4o-mini. Temperature 0.3 vs 0.7. All running simultaneously on real users. With statistical confidence before you commit. Would you like to give it a try for free? Link in comments.

Try now for free: syrin.ai

How SAGE detects internal contradictions: 1. Split output into sentences 2. Embed each sentence via @OpenAI 3. Find sentence pairs with high similarity + opposing meaning 4. Flag semantic negation patterns ("use X" + "don't use X") Zero keywords. Pure embedding math. The scary thing: this fires in production. Real LLMs contradict themselves. Often in the same paragraph.

Flesch-Kincaid Grade Level is a formula from 1975. It detects AI agent drift in 2026. 0.39×(words/sentences) + 11.8×(syllables/words) - 15.59 When an agent drifts, its writing complexity often changes. A technical coding agent suddenly writing like a press release: FKGL drops, drift fires. Old formula. Real signal. No LLM judge required.

@MicroLaunchHQ we'd love more devs to try out syrin.ai

Builders, how can we help you this weekend? Please be crazy.

@mchulet syrin.ai

Hey founders! Looking to connect with people building in: • SaaS • AI • Automation • Web apps • Tech products • Marketing Drop what you're working on 👇

@delveroin syrin.ai

Share your project/website, guys

The weirdest finding from building SAGE👇 Shannon entropy detects overconfidence in LLMs. High-confidence hallucinations → repetitive language → low entropy Uncertain/confused agents → hedge-filled language → high entropy. Both deviate from the agent's normal entropy distribution. Z-score threshold: |z| > 2.0 = high alert. This replaced a hardcoded confidence keyword detector and performs better on every domain.

Our 144-case SAGE benchmark: ✅ Hallucination (obvious): 100% accuracy ✅ Hallucination (subtle): 100% (fixed from 75%) ✅ Clean outputs: 83.3% (3 false positives — corpus quality issues) ⚠️ Goal drift (macro): 61.1% ⚠️ Goal drift (subtle): ~20% Overall precision: 96.7% The gap on subtle goal drift is real. It shares >60% vocabulary with normal outputs. This is an open research problem. #buildinpublic #aiagents

200-agent stress test results: 👉 200 LLM calls, 20 workflows, 5 domains 👉 14 drift alerts (7.78% of handoffs) 👉 0% false positive rate 👉 2.8 seconds per agent 👉 0 crashes Domain drift rates: - Customer Support: 11.1% (highest) - Engineering/Finance/Product: 8.3% - Marketing: 2.8% (lowest) 10-agent pipeline = 50.7% chance of at least one drifting handoff.

@W33Z_global syrin.ai

Happy weekend Founders What are you building this weekend? Let’s send traffic!

@MicroLaunchHQ syrin.ai 💪💪

What are you building this weekend?

@1Umairshaikh syrin.ai

What are you building this Sunday? Drop your projects.

How SAGE V7 compares to earlier approaches: V5 (embeddings) - Hallucination: Good - Coding domain: Partial - Hardcoding: Some - Cost/call: Low - Generalizes: Partial

Our 144-case SAGE benchmark👇 3 domains × 6 agents × multiple test categories. Results by category: ✅ Hallucination (obvious): 100% accuracy ✅ Hallucination (subtle): 100% (fixed from 75%) ✅ Clean outputs: 83.3% (3 false positives — corpus quality) ⚠️ Goal drift (macro): 61.1% ⚠️ Goal drift (subtle): ~20% Overall precision: 96.7% Subtle goal drift shares >60% vocabulary with normal outputs. This is an open research problem.