Shiny Gigs

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Shiny Gigs

Shiny Gigs

@ShinyGigs

https://t.co/QEu3ZOPzd6 connects SAP Experts and Employers. RTs are not endorsements

Santa clara, california, U.S. Katılım Ekim 2022
1.1K Takip Edilen345 Takipçiler
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Career Hound
Career Hound@careerhoundio·
Chabez Tech is now hiring: SAP ABAP with Fiori exp. Consultant - REMOTE Remote | United States
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Career Hound
Career Hound@careerhoundio·
Simple Software Solutions Group is now hiring: Sr. SAP S/4 - SAP FICO / functional - Remote Remote | United States
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Marvin Kaunda
Marvin Kaunda@KaundaMarvin·
Hi everyone, I’m looking for a Product Engineer role in the Bay Area. I have until 4/1 before my visa expires. I know this is a quick turn, but if you’re interested. Please reach out and share! Thank you for your time and help. - Marvin
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Stage 4 Solutions
Stage 4 Solutions@Stage4Solutions·
Needed: SAP S4 HANA Supply Chain Analyst – Solution Architect (Remote) To learn more and to apply, please go to: na2.hubs.ly/H04rDgj0 (Please apply to the SAP S4 HANA Supply Chain Analyst – Solution Architect (Remote)) role #hiring #careeropportunities
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IDI Consulting
IDI Consulting@IDIConsulting·
🚨 We’re Hiring: S/4HANA Utilities Developer 🚨 If you’re passionate about building scalable, integrated SAP solutions in complex enterprise environments, this opportunity is for you. Join a team delivering innovative, enterprise-level impact. Apply now: idi-net.com/careers-at-idi…
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CMETS ENERGY SOLUTIONS LLC
CMETS ENERGY SOLUTIONS LLC@CmetsFze·
We’re Hiring: Accountant Location: United Arab Emirates • Accounting: 5 years UAE Experience (Required) • SAP: 1 year (Required) 📩hr@cmetsae.com cmetsae.com +971 2 559 9549
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Dustin
Dustin@r0ck3t23·
Terence Tao is the greatest living mathematician. Fields Medal at 31. Solved problems that had been open for a century. Widely regarded as the sharpest analytical mind alive. And he just told you the thing your entire career is built on is now worthless. Tao: “AI has basically driven the cost of idea generation down to almost zero.” For five hundred years, the idea was the prize. The theory. The hypothesis. The flash of insight a physicist chased for twenty years in a lab before it landed. That was the bottleneck. That was what tenure rewarded. That was what Nobel committees were looking for. Gone. A model can generate a thousand candidate theories for a scientific problem in an afternoon. Not noise. Not garbage. Plausible, structured, publishable-grade hypotheses. A thousand of them. Before dinner. The idea used to be the scarcest resource in any room. Now it is the cheapest. But Tao went somewhere most people are not ready to follow. Tao: “Verification, validation, and assessing what ideas actually move the subject forward… that’s not something we know how to do at scale.” Sit with that. We automated creation. We did not automate truth. We can produce ten thousand explanations for a phenomenon. We cannot tell you which ones are real. That is not a gap. That is a chasm. And it is the most important unsolved problem on Earth right now. Tao: “Human reviewers… they’re already being overwhelmed actually.” The entire scientific apparatus was built for a world where a single paper took months to produce. Peer review. Journal boards. Consensus forged over years of replication and debate. That infrastructure was never designed for what just hit it. Journals are flooded. Reviewers are buried. The filters that separated signal from noise for decades were engineered for human-speed output. They are now absorbing machine-speed volume. And they are cracking under it. Tao compared it to the internet. The internet drove the cost of communication to zero. That did not produce clarity. It produced an ocean of noise with islands of signal buried somewhere inside. AI just did the same thing to knowledge itself. Infinite generation. Zero verification. The person who can produce ideas has never mattered less. The person who can prove which ideas are true has never mattered more. That is the inversion nobody is processing. Every company, every lab, every institution is racing to generate more. Faster models. Bigger outputs. More theories. More code. More content. Nobody is building the system that tells you which of those outputs are actually correct. And that is the only system that matters. Whoever solves verification at scale does not win a market. They become the filter that all of science, all of engineering, all of human discovery flows through. The bottleneck of the last five hundred years was producing the answer. The bottleneck of the next fifty is knowing whether the answer is real. And right now, according to the greatest mathematician alive, we do not know how to do that at the speed the machines demand. That is not a research problem. That is the race beneath the race. And almost nobody has entered it.
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Om Patel
Om Patel@om_patel5·
THIS GUY AUTOMATED HIS ENTIRE JOB SEARCH WITH AI AGENTS instead of applying manually he built a system with Claude Code that scores every job offer against 10 filters and auto-applies to the ones that match. the agents: > Scanner: discovers listings across 20+ job portals > Evaluator: scores each job A-F against 10 dimensions > PDF Generator: creates an ATS-optimized resume customized per offer > Applicator: fills out application forms automatically via Playwright the 10 filters: > role fit (does the job match one of his 6 target role types) > skills alignment vs. proof points > seniority calibration > compensation range > geographic viability > company stability > product-market interest > growth trajectory > ATS compatibility > timeline urgency results: 516 jobs evaluated. 66 applications sent. 450 auto-rejected with documented reasoning so that the model gets even better. the whole system runs as 12 Claude Code skills. each with its own context doc. the system itself demonstrates exactly the skills the target roles require. multi-agent orchestration, production automation, human-in-the-loop design. this is what job searching looks like in 2026.
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Garry Tan
Garry Tan@garrytan·
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Massimo
Massimo@Rainmaker1973·
The evolution of computers seen by AI
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