Top Tweets for #algorithmengineering
@TeamTrump47 @Nostre_damus We’re not finishing this war that @PeteHegseth started that’s not what we voted for. Now you’re doing really well. Keep it up! I also copied you on Section 230 censorship @nativeone553073 @lynne1171992
Added another layer for /ES 'absorption' CHoCH & BOS reading the tape. Large prints are short covering and legit buying. #Trading #AlgorithmEngineering
@AnthropicAI 👀
My own ladder via API (ticks), trade volume by price, calculates in 10sec increments, will read selling into liquidity, iceberg and tape bombs (Phase 2)
Tonight 0.7t/s overnight session is much different than day/NYC session at 5 to 10t/s+
Currently running /MES ->
Phase 1+
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Control the game… or get controlled by it.
Every move matters. Every decision counts.
Stay sharp. Stay focused. ♠️🔥#AlgorithmEngineering #algotrading #forextrading
The hardest part of algorithm engineering isn’t writing code—it’s making it run reliably on real data. Once a model goes live, you see how different training data is from the re... @karpathy @dragonvcap10317 @DeepMind #AlgorithmEngineering #ModelDeployment #CodeReview #RealData
An algorithm engineer’s daily life is mostly tuning parameters, checking curves, and testing again and again. It may look boring, but every small improvement can push the model a big step forward. #AlgorithmEngineering
Algorithm engineers deal with data, parameters, and metrics every day. It may look boring, but it’s actually exciting. You’re always chasing a better answer, and that answer might be hiding in the next experiment. @ylecun #LabComputer #AlgorithmEngineering #ParameterExperiment
Algorithm engineering isn’t just about writing models—you also have to watch the metrics. Higher accuracy doesn’t mean everything is fine. Recall, latency, and stability are what matter after launch. #accuracy #LaunchTesting #recall #MonitoringMetrics #AlgorithmEngineering
In algorithm engineering, the most important thing isn’t getting it right once—it’s being able to find the problem fast. Is the data off, are the labels wrong, or is the model structure off? Break it down, and the... #DataQuality #TechnicalTroubleshooting #AlgorithmEngineering
Algorithm engineering is more than writing a few lines of code. It’s like tuning a precision machine. Where data comes from, how features are chosen, and how the model is trained all affec... #CodeTerminal #FeatureEngineering #TuningInterface #TrainingLogs #AlgorithmEngineering
Algorithm engineering doesn't end when the code is written—it really starts after launch. Reading logs, watching metrics, checking anomalies, running regressions. Every day is about working with data, and details decide the outcome. @ylecun #AlgorithmEngineering
Algorithm engineering sounds hardcore, but the core is really three things: clean data, solid features, and stable results. Change one parameter today, and you... @huggingface @kdnuggets @RetorS20974 #TrainingData #AlgorithmEngineering #FeatureEngineering #TuningPanel #TestSet
Algorithm engineering sounds hardcore, but it really comes down to one thing: making models more accurate, stable, and fast. Tuning, training, evaluation, deployment—every step is a battle with d... @Kaggle @BCoder8709 #ParameterTuning #AlgorithmEngineering #TestSet #CodeScreen
Algorithm engineering isn’t magic. Most of the time, it’s about polishing a small problem again and again until it’s perfect. If the data is dirty, clean it; if the... @flux_e58062 @DeepLearningAI #AlgorithmEngineering #lab #DataCleaning #IterationOptimization #ModelRetraining
What is algorithm engineering like? It's like tuning an AI's temperament. You clean the data, choose features, tune parameters, and validate the model again and again. It looks ha... @ylecun #ModelValidation #DataCleaning #ParameterTuning #AlgorithmEngineering #FeatureSelection
A lot of people think algorithm engineering is just hyperparameter tuning, but it's much more than that. Data cleaning, feature design, online monitoring, rollback plans—every step matter... @awn_b75350 @ylecun #RollbackPlan #DataCleaning #AlgorithmEngineering #OnlineMonitoring
In algorithm engineering, the real test is not whether the model runs. It’s whether production stays safe. Good offline metrics do not mean the real world will be stable. If you can handle testing, monitoring, and rollback we... @kdnuggets #TestEnvironment #AlgorithmEngineering
One of the most important skills for an algorithm engineer is breaking complex problems down. Start with the data, then the features, then the model—step by step, ma... @karpathy @ErCiph62477 #FeatureProcessing #ProblemSolving #AlgorithmEngineering #ModelDebugging #DataAnalysis
Whether algorithm engineering is good shows up in the user experience. Are recommendations accurate? Is response fast? Are results stable? On the surface it's UX, b... @LSihft30630 @ylecun #AlgorithmEngineering #UserExperience #StableResults #RecommendationSystem #ResponseSpeed
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