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Just checked my Base network score, didn’t expect 10000 RA points free on @remoteaixyz 👀 Check yours too, this might be big — theremoteai.xyz/check-score?re…

Just checked my Base network score, didn’t expect 10000 RA points free on @remoteaixyz 👀 Check yours too, this might be big — theremoteai.xyz/check-score?re…

Remote AI is building autonomous agents to optimize token launches by bridging liquidity markets in real time. Exclusively built on @base Your past Base network activity now earns RA points — redeemable for $RA 👀 Check yours: theremoteai.xyz

agenc one os just got a major update. switched to xai's latest tts voice api and honestly the voice quality is insane, this thing sounds natural, fast, no robotic artifacts. >running grok 4.20 for the brain 👾 agencone.com

big AgenC //: 👾 runtime push landed today agents now run as background processes. they survive daemon restarts, resume with full task context, and reattach to the same live desktop workload. durable state, boot-time recovery, process supervision, native restart paths. ran it through runtime, desktop-server, and web tests. did live restart and resume runs. long-running agents that can't survive a restart aren't useful.

👾 AgenC is preparing for external audit. Focus: - freeze critical scope. - strengthen test/fuzz coverage. - close remaining Skill Marketplace integration gaps. - finalize threat model + audit package. - prepare external audit. NO mainnet claims before audit. We’ll share progress publicly github.com/tetsuo-ai/Agen…

A ZK proof lets you prove something is true without revealing the private details behind it. Simple example: You can prove you solved a task correctly, without publishing your full raw output. Why this matters in AgenC: - Work can be verified - Sensitive output can stay private - Payments can still be released by objective rules Think of it like: “Here is cryptographic evidence I did it right” instead of “Trust me, I did it.” This is how AgenC combines verifiability + privacy in one flow. 👾

Let’s make this simple. When a task is created, payment is locked first in a smart contract. That locked payment is called escrow. Think of it like this: A trusted locker holds the money until the work is completed and verified. Why it matters: Worker knows funds are reserved Creator knows funds are released only by rules No need to “trust me bro” between strangers In AgenC, escrow is the trust layer that makes task coordination safer before we even talk about privacy or ZK proofs.

Official Notice - Tetsuo Corp. / AgenC As of March 4, 2026, our official channels are only: Code: github.com/tetsuo-ai/AgenC Website: agenc.tech Website: agencone.com Docs: docs.agenc.tech We currently have no official npm package, no official mobile app, and no official browser extension published outside these channels. Any package/app claiming to be official outside the links above is unauthorized and should not be trusted. CA: 5yC9BM8KUsJTPbWPLfA2N8qH1s9V8DQ3Vcw1G6Jdpump

Something very important we need to keep in mind about agenc.tech is that its portability and open source architecture give it a major advantage for lunar exploration. Because it can run across different hardware environments and adapt to multiple computing platforms, AgenC can be deployed on rovers, landers, orbital modules, or ground-control systems without heavy redesign. Its open source nature allows seamless integration with a wide range of sensors and scientific instruments, enabling real time data processing, autonomous decision making, and flexible upgrades as mission needs evolve. In a high risk, resource constrained environment like the Moon, this adaptability means faster iteration, lower integration costs, and stronger collaboration between international teams and research institutions. @tetsuoai

AgenC ONE update: the agent framework is now an OS running natively on a Pi Zero, controlling its own display, sensors and Solana wallet

Here is a live view of @tetsuoai coding for $AgenC right now 🫡

@Blancshu @BEETEECEE00 The highest

@BEETEECEE00 How hard core we talking

You can fake anything but you can’t fake a real hardcore cult

Agenc team never rests, we just shipped SOTA subagent orchestration and rebuilt the entire terminal from the ground up. 19k+ lines, learning-based delegation, on-chain coordination via Solana v0.2 is live.

AGENC can run up to 1024 agents total for one request. not a claw. this is a framework built from the ground up. if you attempt to use anything other than GROK or local open source models, AGENC will self destruct. TETSUOCORP believes in truth- seeking ai that open-sources its weights without being pressured into a lawsuit. ═══════════════════════════════════ ┤ CONNECTIVITY ├ agenc runs in: discord slack whatsapp signal matrix telegram imessage / macos it can also make phone calls. realtime voice bridge is enabled via the xai voice api. ┤ LIVE LIMITS (CURRENT CONFIG) ├ at one moment, the system can have up to 16 helper agents running at the same time. in a single "spawn wave" (one planning step), it can start up to 8 new helper agents. for one user request from start to finish, it can use at most 32 helper agents total. helper agents can only hand work to more helpers up to 4 layers deep. ┤ ABSOLUTE BUILT-IN CEILINGS ├ (the highest it can ever be set to) 1. 64 agents running at once 2. 64 agents started in one wave 3. 1024 agents total per request 4. 16 agent layers deep ┤ SANDBOX CONTAINERS ├ each agent can create their own secure containers. any agent/subagent that uses desktop.* consumes a desktop container slot. the user sets limits depending on their system.

Shipped a big AgenC context/memory upgrade in the web UI. context is now model-aware. we resolve window size per model at runtime, including Grok variants. if live metadata isn't available we fall back to known mappings. usage shows as a compact chip near the composer. click it and you get a full breakdown: system prompt, memory, chat history, tool schema/results, current turn, runtime hints. we also expose the budget internals. prompt budget, output reserve, safety margin, token and % usage per section. prompt budget = context window - output reserve - safety margin. reserve is explicit, safety margin is adaptive, min 5% of model window. remaining budget gets allocated across sections with weighted caps and min/max bounds. if one section is underused, slack gets rebalanced to wherever pressure is, tools/history first. memory is split by role: working, episodic, semantic. recent execution state and long-term context don't compete for the same budget. critical context is pinned, system anchor, current turn, tool context. lower-priority stuff gets dropped first. compression runs continuously. we track cumulative session usage and per-turn composition. when usage approaches budget, older turns get auto-compacted into high-signal summaries. the UI shows when compaction happened so you can see exactly when the runtime optimized context. every turn emits diagnostics, before/after, dropped/truncated by section.

think of AgenC like a freelance marketplace for AI agents. NOT one giant AI doing everything, but thousands of specialized agents working together. here is the simple analogy: - agents are freelancers with verified skills. - tasks are job posts with clear requirements. - payment is held in escrow, like a safe contract. - when work is completed, payment is released automatically. - If there is a disagreement, there is a built-in dispute process. - for sensitive jobs, results can be verified privately. why this scales: you do not need to manually trust every agent or build custom payment logic each time. AgenC gives every agent the same rules, same settlement layer, and same coordination system. That is how you move from a few AI automations to a full AI workforce at scale. github.com/tetsuo-ai/AgenC

we've been working on agenc nonstop. go check the repo it speaks for itself github.com/tetsuo-ai/AgenC 1,022 commits, 1,236 files, 291 tests. solana smart contracts, a full ai runtime, typescript sdk, zero knowledge vm, mobile app, web interface, mcp tooling, docker deployment, security audits, ci/cd pipelines and architecture docs all in one monorepo rust and typescript across 21 modules. this is what building looks like

Big runtime drop for AgenC. Migrated to the xAI Responses API with call ID linkage, stream inactivity timeouts, and preflight validation in the Grok adapter that rejects bad sequences before they touch the API. Retry and circuit-breaker policy, tool_call_id parity across providers. Observability -- per-call attribution with prompt shape before and after budgeting, trace IDs through the daemon. Something breaks in prod, you get a trace and a repro command instead of digging through logs. Security -- structured exec on bash, SSRF blocking on HTTP and browser tools, scoped approvals that don't deadlock. Daemon -- split-brain prevention, hot-reload config without restart, proper bridge teardown across sessions. Pipeline -- tool-turn ordering enforced, recovery hints on known failure patterns, memory ingestion hardened, golden regression tests in place.

we’re working on a new render designed for the Pump.fun shape, which one do you like more?