Aleξξξ retweeted
BTW anyone not using agents like this is falling behind and will inevitably wind up in the permanent underclass.
Aleξξξ retweeted
🇫🇮Χαμός στη Φιλανδία που η Google έκοψε αυθαίρετα 400,000 δέντρα για να κατασκευάσει data center χωρίς να έχει ολοκληρωθεί περιβαλλοντολογική μελέτη. Το κράτος σταμάτησε άμεσα την κατασκευή.
Στην Ελλάδα θα φωνάζαμε Α & ΟΥ & ΔΑΠ ΝΔΦΚ με την μίζα στην τσέπη
teapaw.org/AFP/status/21125748698…
🇫🇮 Finnish town braces for change from massive Google data centre
With its cool climate and relatively stable and cheap electricity, Finland has seen a spike in data centre investments.
But as dozens of the energy guzzling centres are now under construction and many more planned by companies including Google, Microsoft and TikTok, uncertainty around energy prices, grid capacity and environmental effects have sparked a public debate in Finland
Aleξξξ retweeted
Europe's best climates, by how many days per year of truly nice weather you get.
Canary Islands always FTW.
I listened to your feedback, so this map is different: it rewards how many months sit in the 20–28°C window, and how often those days are sunny, regardless of peaks or excessive swings.
In the end, great weather all year round isn't less good because one month is “too hot”, at least that's what I think...
Europe's best climates and where to find them, updated.
Calabria FTW, Sicily not so much... Cyprus is surprisingly out because of very hot summers. Madeira and the Canary Islands remain near perfect, of course.
This is the accurate data, with stricter parameters. Apologies for the mix-up: I was on mobile all day and crossed the order of the maps. I meant to publish a comparative 2000-2020-2050.
Not too bad, but still, this is the one truly on point
Aleξξξ retweeted
Replying to @MistaksWerMade
What problem did this solved during those times.....?
This quoted post is unavailable.
Aleξξξ retweeted
#1 on github trending now...I do what I can😂
Aleξξξ retweeted
this is what a european datacenter looks like
Replying to @GuillaumeLample
ML4 was trained on 3,800 NVIDIA Grace Blackwell GPUs in our European datacenters -- our cluster in Bruyères-le-Châtel built using our Series B fundraise.
We are investing heavily in infrastructure, with our Series C and D clusters coming online soon. This additional compute will let us train longer, pursue more ambitious post-training, and iterate faster.
We expect large and rapid improvements in the weeks and months ahead.
2/n
Readers added context they thought people might want to know
The posted photo shows the Breuillet - Bruyères-le-Châtel train station in France, not a datacenter. Mistral AI's cluster is in a separate modern modular facility operated by Eclairion at the location.
fr.wikipedia.org/wiki/Gare_de_B…
datacenterdynamics.com/en/news/mistra…
usinenouvelle.com/article/ce-dat…
baxtel.com/data-center/ec…
Aleξξξ retweeted
I gave Claude another 18 hours.. and I think this one is the best one yet
Macrohard: Windows XP
I'm blown away
Aleξξξ retweeted
Replying to @ClaudeDevs
Had Opus 5.5 make a video, insane. @bcherny @trq212 @lydiahallie @adocomplete @claudeai @AnthropicAI
Aleξξξ retweeted
My god this is such a good speech that every SWE needs to hear. You know what? Every person should hear it
Keep the happy memories, eyes on the reality, be excited about the future. That’s the best that anyone can do
Aleξξξ retweeted
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
Aleξξξ retweeted
If you can build these 12 Forward Deployed Engineer projects.
You're hired.
Project 1 :) Bi-Directional Legacy Sync Engine
Real-time sync between your modern SaaS and a customer's 20-year-old on-prem SQL database with conflict resolution.
➣ Shows : You can bridge modern AI with messy, real-world enterprise data.
Project 2 :) Zero-Trust Multi-Tenant RAG
Vector database with strict row-level security where Tenant A's AI can never see Tenant B's embeddings.
➣ Shows : You can deploy AI without leaking data across customer boundaries.
Project 3 :) PII Redaction & Unmasking Proxy
Middleware that intercepts LLM traffic, masks PII before it hits the model and unmasks it on the way back.
➣ Shows : You understand enterprise privacy and compliance (HIPAA/SOC2).
Project 4 :) Air-Gapped One-Click Deployer
Helm charts and scripts that deploy the entire AI stack into a secure, offline customer VPC in under 30 minutes.
➣ Shows : You can ship software into highly secure, restricted environments.
Project 5 :) ROI & Usage Telemetry Dashboard
A real-time dashboard showing the customer exactly how many hours and dollars the AI saved them this week.
➣ Shows : You prove product value to the customer's CFO not just their engineers.
Project 6 :) Idempotent Webhook Reconciler
An event processor that handles out-of-order webhooks, retries with exponential backoff and dead-letter queues.
➣ Shows : You build reliable integrations that survive network chaos.
Project 7 :) Enterprise SSO & SCIM Bridge
Automated user syncing between the customer's Okta/Azure AD and your app, handling role changes instantly.
➣ Shows : You can pass enterprise IT and security reviews.
Project 8 :) AI Fallback & Degradation Gateway
An API gateway that automatically falls back from Opus → Sonnet → Local Llama → Cache if the primary model times out.
➣ Shows : You protect the customer experience when upstream APIs fail.
Project 9 :) Automated Compliance Auditor
A script that continuously checks customer deployments against security controls and generates a readiness report.
➣ Shows : You treat security as code not a manual checklist.
Project 10 :) Custom MCP Server for Legacy ERPs
A Model Context Protocol server that lets an AI agent safely query and update a customer's SAP or Oracle instance.
➣ Shows : You can connect AI agents to the tools enterprises actually run on.
Project 11 :) Shadow Traffic Evaluator
A tool that duplicates 5% of production customer traffic to a new AI model silently, comparing outputs without affecting the user.
➣ Shows : You can safely test and upgrade models in production.
Project 12 :) 3 AM Incident Runbook Repo
A fully interactive, tested playbook repository that allows a junior engineer to rollback, scale or debug a customer outage at 3 AM.
➣ Shows : You build systems that others can recover when things break.
Most people stay stuck watching tutorials. Builders get hired.
bookmark & repost !
If I had 6 months to become a Forward Deployed Engineer.
I'd do this.
Stage 1: Python and API Foundations
FastAPI, async, error handling, webhooks, REST/GraphQL, third-party SDKs.
Stage 2: Customer Integration Patterns
OAuth2, SAML, SSO, Salesforce/Slack/HubSpot APIs, webhook verification, retry logic.
Stage 3: Multi-Tenancy and Data Isolation
Row-level security, tenant identifiers, separate databases vs schemas, data partitioning.
Stage 4: Security and Compliance Basics
SOC 2, GDPR, HIPAA, PII redaction, encryption at rest and in transit, audit logging.
Stage 5: RAG for Customer Data
Secure ingestion pipelines, access control per document, citation tracking, permission filtering.
Stage 6: Deployment Automation
Docker, Kubernetes, Helm charts, CI/CD pipelines, one-click customer environments.
Stage 7: Observability and Dashboards
Distributed tracing, customer-facing health pages, usage metrics, SLA monitoring, alerting.
Stage 8: Incident Management and Debugging
Production debugging, log analysis, rollback strategies, post-mortems, customer communication during outages.
Stage 9: Documentation and Runbooks
Deployment guides, API docs, troubleshooting playbooks, onboarding checklists, knowledge base articles.
Stage 10: Requirements Gathering and Translation
Talking to non-technical stakeholders, translating business needs to technical specs, managing expectations.
Stage 11: Customer Success and Adoption
Usage analysis, feature adoption tracking, identifying expansion signals, reducing churn, proving ROI.
Stage 12: Public Portfolio and Case Studies
Document deployed projects, write implementation guides, share integration patterns, publish success metrics.
Most people stay stuck watching tutorials.
Builders get hired.
(Bookmark it)
Aleξξξ retweeted
I wonder if there is an @xkcd comic for this...
Aleξξξ retweeted
we built pdf-inspector so agents can process PDFs without waiting on OCR. it classifies any PDF in ~20ms and extracts clean markdown locally
→ 200 PDFs processed in 2.8s
→ top quality in extracting tables + graphs
→ built in rust
→ open source
github.com/firecrawl/pdf-ins…










































