Hicap.ai

Exciting news: Hicap is now live in Cline as a BYOK provider. Gemini models: 25% off Exclusively for @cline users. Ends in 5 days. No credit card required to start. Same Google Gemini API. Just cheaper & faster. hicap.ai/promo

Want to try it? 👉 hicap.ai

Hicap Voice is here to stay. One API key. A full suite of voice models. Manage your voice models where you already manage your LLMs. We didn't build another voice API, we built the last one you'll need.

Gemini 3.5 Flash is now live across the Hicap API! Frontier performance for agents and coding - 4x faster than other frontier models, and Google's strongest coding model yet. Available in: - Cline - OpenClaude - ClaudeCode Router — and more!

Check out Cline on product hunt! producthunt.com/products/cline…

Agent infrastructure is becoming modular fast. @cline just open-sourced an SDK for building portable coding agents across apps, IDEs, CLIs and more. Hicap is proud to support it as an inference provider. Build your agents. We’ll handle the inference layer.

Step-by-step: setting up Hicap as your provider.

Congrats to the @Cline team on the launch! Try adding Hicap as your model provider in Cline.

Teams spend months on this. Hicap collapses it to one line of code.

The old way is a 13-step headache. Capacity allocation, provider contracts, rate limits, failover routing, multi-provider billing.

Plug in. Save 25% on inference. Nothing else changes. Same models. Same performance. Actually faster. More secure. So how?

@cline Huge day for Cline team ⚡ Well deserved, congratulations!

Introducing the Cline SDK. We rebuilt the Cline harness for our extension and CLI from scratch using all the lessons learned since creating one of the world's first coding agents in 2024, and are open sourcing it for others to build with today. npm i @​cline/sdk 🧵

Stat 3: There’s also an emerging “LLM tech debt” issue. Teams keep adding new models, but rarely retire old ones, which creates invisible cost accumulation, operational complexity, and governance challenges over time. As adoption matures, infrastructure management, visibility, and reliability are starting to matter just as much as the models themselves.

60% of all LLM errors in production were rate limit failures, with 8.4 million occurring in a single month alone. It’s interesting because most conversations around AI infrastructure focus on model quality or hallucinations, while a lot of the real production pain seems to come from capacity and throughput constraints at scale.

Datadog just published their State of AI Engineering report. LLM data from 1,000+ customers in production. A few numbers that we felt were important. 70% of organizations are now running 3+ models simultaneously. Most teams have effectively built model portfolios, but they’re still managing them through fragmented API calls and disconnected infrastructure. The report itself points toward the growing need for gateway-layer orchestration as multi-model usage becomes the norm. That's why we're building Hicap. See more details below 🔽 Link to full article: datadoghq.com/state-of-ai-en…

Meet Ben Green, our Co-founder and CPO. With 10 years of expert experience in software engineering at PayPal, Honey, and Stell, Ben has worked on teams of every size, from two people to twenty thousand+, and knows exactly what it takes to scale. At Hicap, Ben leads product, making enterprise-grade AI faster, cheaper, and accessible to builders

@ycombinator Applied to YC yesterday. Bumped into @garrytan @stanford a few mins ago. I don't believe in coincidences. @hicap_ai 🫡📷🚀

To get started with OpenClaude 1. Go to platform.hicap.ai and make a key 2. Run npm install -g @gitlawb/openclaude 3. /provider 4. Select hicap 5. Enter the model like gpt-5.5 and your key 6. Start coding!! 🥳🥳