
ACL- Adaptive Context Layer
The missing reliability layer for production AI.
ACL- Adaptive Context Layer launched on April 27, 2026 with 10 votes, #1,251 of 21,248 launches that month and more than 90% of that year's launches.
What it does
LLM gateways ignore silent failures, loops, irrelevant outputs & wasted tokens. ACL makes your AI 2x more productive - delivering exactly what you mean, faster. Powered by the Kepler Intelligence Layer, ACL filters noise, recovers failures, detects & breaks loops, and tracks every token in real-time. One API key. No code changes. Works with every LLM & gateway. Benchmarked across 7 models - 17/18 categories won. Free tier. Input-only billing.
ACL services have been permanently discontinued.
As a result, all public ACL services, applications, APIs, dashboards, and website functionality have been permanently discontinued and are no longer available for public use. We sincerely thank all users, customers, partners, and supporters who trusted ACL throughout its journey. The technology will continue as part of the acquiring organization's internal infrastructure andfrom acl.fridayaicore.in
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TrueFoundry AI GatewayDec 2025 · truefoundry.com · ▲315Many teams connecting LLMs to external tools eventually encounter the same architectural issue: as more tools and agents are added, the integration pattern becomes an N×M mesh of direct connections. Each agent implements its own auth, retries, rate limiting, and logging; each tool needs credentials distributed to multiple places and observability becomes fragmented. We built LLM gateway with this goal to provide a single place to manage authentication, authorization, routing, and observability for MCP servers, with a path toward a more general agent-gateway architecture in the future. The…
Context GatewayMar 2026 · github.com · ▲220We built an open-source proxy that sits between coding agents (Claude Code, OpenClaw, etc.) and the LLM, compressing tool outputs before they enter the context window. Demo: https://www.youtube.com/watch?v=-vFZ6MPrwjw#t=9s. Motivation: Agents are terrible at managing context. A single file read or grep can dump thousands of tokens into the window, most of it noise. This isn't just expensive — it actively degrades quality. Long-context benchmarks consistently show steep accuracy drops as context grows (OpenAI's GPT-5.4 eval goes from 97.2% at 32k to 36.6% at 1M…
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Ratel, give agents unlimited tools and skills without context bloatJul 2026 · github.com · ▲23Hi HN! We're Giacomo and Roberto, authors of Ratel (https://github.com/ratel-ai/ratel) We used to help SaaS companies build agents on top of their products. Whenever we wanted to expand the agents’ complexity/scope, by adding more and more tools and instructions, we always run in the same issue: context bloat, with frequent hallucinations and sky high token bills. So we started constantly engineering the agents, dynamically loading tools, splitting them into subagents, inventing our own way to support skills And that's exactly when we started building Ratel: a…
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