4.0K
MAGet past some paywalls with this trick
@mattupham
#Trusted Platform Module Tpm Explained
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ITComment "TPM" and I'll send you the full breakdown directly. 👇
The AI Technical Project Manager role is one of the fastest-growing in tech right now.
And most people are approaching it completely wrong.
They're chasing certifications before they understand how AI systems actually work in delivery.
The role sits at the intersection of three things, AI literacy, technical delivery, and stakeholder communication.
Miss any one of them and you'll struggle to add real value in the room.
The good news is you don't need to be an engineer to get there, you just need the right roadmap.
Comment "TPM" and I'll send you the full breakdown directly. 👇
[ai, technical, project, manager, career, roadmap, tech, 2026, tpm, projectmanagement, aicareer, growth, learning, leadership, skills, viral, fyp, explorepage]
#aitechnicalprojectmanager #technicalprojectmanager #techcareer #projectmanagement #aicareer
@itsgoharzaman
376.5K
DYsend this to the group chat…someone in there needs this 🤣🙏🏼#study #studytools #college
@dylan.fastr
1.1M
NAक्या आपकी टोटल प्रोडक्टिव मेंटेनेंस (TPM) पहल नाकाम हो रही है? इसकी एक बड़ी वजह यह हो सकती है कि आपने इसकी ज़िम्मेदारी गलत व्यक्ति को सौंप दी है!
🚨 सबसे बड़ी गलती: TPM की बागडोर मेंटेनेंस मैनेजर को देना।
मेंटेनेंस का काम: टूटी हुई मशीन को ठीक करना।
TPM का काम: मशीन को टूटने ही न देना!
ये दोनों दो अलग-अलग काम (Jobs) हैं।
✨ TPM का असली मालिक कौन?
यह है प्रोडक्शन मैनेजर! क्यों? क्योंकि मशीन का अपटाइम (uptime) सीधे उनके प्रोडक्शन टारगेट को प्रभावित करता है।
✅ सही रणनीति:
ऑपरेटर को मशीन का मालिक (Owner) बनाएं।
उन्हें सफ़ाई (Cleaning) और छोटे चेक-अप्स (Small check-ups) की आदत डलवाएं।
जब ऑपरेटर्स मशीन की हेल्थ (Health) का ध्यान रखते हैं, तो ब्रेकडाउन (Breakdown) ज़ीरो हो जाते हैं!
TPM एक प्रोडक्शन टूल है, न कि सिर्फ़ एक मेंटेनेंस टूल!
💡 अपनी TPM रणनीति को बदलने और इसे सफल बनाने के लिए "TPM" कमेंट करें!
#TPM #TotalProductiveMaintenance #Manufacturing #Production #Maintenance #Industry40 #LeanManufacturing #ContinuousImprovement #OperationalExcellence #FactoryLife #NarinderChandok #उत्पादन #फैक्ट्री
@narinderchandok
16.8K
METPM on paper is easy.
Files look perfect, audits get cleared, and posters are everywhere.
But real TPM starts only when people follow standards, machines are maintained daily, and losses are attacked on the shop floor — not in meetings.
If TPM exists only in documents, it’s not TPM.
It’s decoration.
#TPM #TotalProductiveMaintenance #TPMReality #ManufacturingLife #ShopFloorTruth #IndustrialEngineering
@mechnode
31.5K
ASCOMMENT Guide for plateform link - 100 GB
@ashwani.builds
296.9K
GU✨Salah satu cara paling simple dan aman buat turunin penggunaan CPU di laptop💻 kalian dengan tools ini.
tinggal buka powershell, lalu ketik command ini : "irm christitus.com/win | iex" Enter✨.
hasil nya pengguanaan CPU bisa turun drastis dan laptop kalian gabakal lemot dan kepanasan lagi💯
⚠️ Save video ini biar ga lupa, comment di bawah kalo cara ini berhasil diblaptop kalian.
#laptopsecond #tutoriallaptop #technology #tipswindows #gudanglaptop_id
@gudanglaptop_id
8.7K
FRAprenda SAP PM dentro da formação key user✅
@freirecruzoficial
36.4K
TEIn practice, a Mixture-of-Depths (MoD) model is not some totally new “type” of model, it’s usually a regular transformer (like Llama/GPT) where engineers have modified the forward pass so each token can stop going through layers once it has been “solved.”
Concretely, if you open the code in PyTorch or JAX, you’ll still see a stack of layers [Layer 1 … Layer 48], but around that stack there’s an extra router module that runs at each layer: it looks at the hidden state of every token and outputs a number like “keep thinking” or “you can exit now.”
During the forward pass, you start with all tokens active; after, say, Layer 6, you call the router, and for tokens with an “easy” score you mark them as finished and stop passing them into deeper layers (you just carry their current hidden state forward untouched). The remaining “hard” tokens keep going through Layer 7, 8, 9… with the same check repeated a few times. Implementation-wise this shows up as masks or indexing inside the model: at each depth you filter which token positions are still active, and the rest are skipped from computation.
To train these models, people either train from scratch with an extra loss that encourages sensible halting, or they distill from a full model: you freeze the original LLM, add the router, and train it so that “early-exited” tokens mimic what the full 48-layer run would have produced. From your app’s point of view nothing changes, you still call model.generate() or model(input_ids), but under the hood the MoD version does less work on easy tokens, so it returns faster and uses less memory.
In code terms, MoD is basically a smart scheduling policy inside the transformer loop, not a new API: one extra small network (the router), a halting rule, and a modified loop over layers that can skip computation for some tokens.
.
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@techwithnt
156.2K
KHLink in Highlights 🫶🏻
Want to actually code machine learning instead of just understanding theory?
Today’s video is about TensorFlow:
one of the core libraries used to build and train ML models.
From defining tensors
to solving real ML problems,
this is where concepts turn into code.
If you’re serious about learning AI, start practicing.
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@khushigrewall
254.1K
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@pythonboyz
36.5K
RUManual testing panni panni tired ayitingala? 😫 Meet the Dynamic Duo: Playwright MCP + Chrome DevTools MCP.
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@ruralbytestamil
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