ai•Lab

@ailab.ee

15 Followers 4 Following

Building practical AI solutions for complex problems. No hype, just engineering. We build custom applications, autonomous agents, and provide real-world AI strategy.

AI Lab infographic comparing LLM API prices in USD per 1 million tokens, shown as input/output, with Artificial Analysis Intelligence Index v4.1 scores. Rose represents proprietary models; teal represents open-weight models. Proprietary: GPT-5.6 sol—59, $10/$45; terra—55, $5/$22.50; luna—51, $2/$9; Claude Fable 5—60, $10/$50; Claude Opus 5—61, $5/$25. Open-weight: Kimi K3 via Moonshot AI—57, $3/$15; GLM 5.2 via Z.ai—51, $1.40/$4.40; StreamLake—51, $0.7525/$2.365; AkashML—51, $0.77/$2.42. Main findings: median open-weight output price is $3.41 versus $25 for proprietary models, 7.3 times lower. At index 51, StreamLake costs $2.365 output versus $9 for GPT-5.6 luna, 3.8 times lower.
Bar chart titled “Cost per Task,” showing weighted average cost in USD per Intelligence Index task; lower is better. Costs rise from DeepSeek V4 Pro at $0.04 and GPT-oss-120b at $0.06 to MiniMax M3 at $0.12, Nemotron 3 Ultra at $0.24, Muse Spark 1.1 at $0.26, Grok 4.5 at $0.31, GLM-5.2 at $0.32, Gemini 3.6 Flash at $0.50, Kimi K3 at $0.95, GPT-5.6 at $1.04, and Claude Fable 5 with fallback at $2.75.
X.com post by Peter Steinberger (@steipete): “Here’s your monthly reminder that you shouldn’t be prompting coding agents anymore. You should be designing loops that prompt your agents.” Timestamp: 21:58 · 07/06/2026 · 4.3M Views. Engagement: 1.3K replies, 1.9K reposts, 16K likes, 12K bookmarks.
Dashboard screenshot with status bar: “Firewall,” “Agent Engine,” “8 agents,” “0 pipelines,” and button “START PIPELINE.” Main panel “RESEARCH & TRIAGE” shows 111 Sources, 794 Entries, 231 Threats, 8 Engines, 15 Engine Limited; status line: 2 Awaiting, 0 Queued, 0 Triaging, 225 Promoted, 0 Dismissed, 5 New (24h). “AGENTS (24H)” lists: Researchers 0 Pending, 0 Running, 0 Done, 0 Failed, 0 Timeout; Discoverers 0 Pending, 0 Running, 4 Done, 0 Failed, 0 Timeout; Triagers 0 Pending, 5 Running, 76 Done, 135 Failed, 0 Timeout; Pipeline 0 Pending, 0 Analysis, 0 Rules, 0 Testing, 12 Done, 3 Failed, 0 Cancelled. “NEW ITEMS ADDED” table: Sources 24H 2, 7 DAYS 11, 30 DAYS 71; Researches 5, 32, 417; Threats 34, 35, 231; Rules 29, 30, 262. Right-side panels: “PIPELINES” Active 0, Completed 59, Total 74; “RULES” Draft 0, Testing 5, Approved 206, Published 0, Total 262; “LEARNER” Completed (7d) 3, Findings (7d) 15, Changes (7d) 10, Total runs 37.
iMessage conversation with Andy. Andy: “Looks like food wasn't logged today — hard to track the deficit without it. Even a rough log helps.” (2m) Reply: “It was. But I guess the sync hadn’t run yet. Is the data there now?” (Now) Andy: “Could you remind me what you're referring to? I want to make sure I check the right data — the last few topics were moving quotes, health stats, and earlier the morning brief. Which sync are you asking about?” (Now)
Chat screenshot message: “Sending to 10 more providers now. Also — Moving24 already replied while I was working on this: • €85/h + VAT — 2 movers + truck • Free packing materials included • Available. So you’ve got one quote in already. I’ll flag the others as they come in.”
Audio player controls (play button, “0:03 | 11:00,” volume icon, star button, double-down arrow button, and “1x”) above text that reads: “If you haven’t come across OpenClaw yet, here’s the short version: it’s an open-source personal AI assistant that runs on your own machine. Mac, Windows, or Linux. You talk to it through whatever chat app you already use”
Support Agent web app screenshot showing “Flow Builder: Support Ticket Flow.” Left sidebar menu: Dashboard, Runs, Flows (selected), Agents, Tools, Prompts, Plugins, Users, Settings. Step Types panel lists: Filter (“Filter or validate input data”), Agent (“Execute an AI agent”), Decision (“Branch based on conditions”), Validation (“Validate output with LLM”), Action (“Execute external action”), Transform (“Transform data format”). Main canvas flow: “Classify Ticket” (Agent, Entry) → “Check Response Needed” (Decision) branching to “Research & Process” (Agent, highlighted) or “Close Ticket” (Action); “Research & Process” → “Check Escalation” (Decision) branching to “Escalate Ticket” (Action) or “Validate Response” (Validation) → “Compose Final Response” (Agent). Right panel shows “Research & Process” settings: Name “Research & Process,” Entry Point toggle, Select Agent “Test Support Agent,” link “View/Edit Agent Configuration,” Connections with Incoming (from) “Check Response Needed” and
Social media post composer screen with buttons “Cancel” and “Post” and share icons; text box labeled “Post 1” with counter “162 / 280” contains: “It doesn’t always have to be a large and complicated application to improve the user experience with AI. Sometimes just a tiny addition will add the most value.” A “Proofread” button is highlighted, with a green arrow pointing to it.
Flowchart of an AI-assisted customer support workflow. Left panel “Tools & Subagents” lists: Support Tickets History, Knowledge Base, Client Profile, Error Log reader, Config Reader, DNS Lookup, Web Reader, Client Payments, Navigation Instructions, System Status, Internet Search, Company Website, Client Invoices, Debugging Agent. Top right “Ticketing System” has “New Message” and “Send Reply.” Middle right “Filters” decision path: “Recognize Client?” NO → “Reply asking to authenticate.” YES → “Is Ticket Spam?” YES → “Mark as spam” NO → “Needs Response?” NO → “Archive Ticket” YES → AI Agent Core. Bottom right “AI Agent Core”: “AI Agent” → “Validator” (loop labeled “Solution inadequate” back to AI Agent; “Solution adequate” continues) → “Composer.” Bottom left “Escalation Detection”: “Escalation need detected” → “Who?” → “Assign & Message Tehnical Team” or “Assign & Message Billing Team,” then line “Notify About Escalation.” Logo text “ai-Lab.”