Ananias Esteves @notapply99

@1769_alex That’s all we need , a reason to break above 750 to ATH….. it’s taco news market season

@jbkingflow Too many crazy possibilities on 5 minutes timeframe Bullish pennant $762 target , rectangle 763 &728, double top $726. Rising edge $711 🤯🤷🏻‍♂️🤷🏻‍♂️🤷🏻‍♂️🤷🏻‍♂️🤷🏻‍♂️🤯🤯🤯🤯 $SPY

pump: $BRN.AX / $BRCHF — BrainChip. 6 months is hard to time, but if Physical AI and edge inference become the next bottleneck, I think BrainChip has real 10x potential over time. Akida is not trying to replace GPUs. It is targeting a different problem: ultra-low-power, always-on AI at the edge — sensors, robotics, drones, satellites, defense, automotive and industrial systems. The cluster trains intelligence. The edge deploys it into the real world. If BrainChip executes, this could be one of the most asymmetric AI names out there. Not financial advice — just my highest-conviction small-cap AI bet. @BrainChip_inc

Recent third-party coverage on BrainChip’s Akida Communication Reference Platform. This is the exact edge AI bottleneck thesis: real-time RF signal classification under 1 watt, using Akida AKD1500. Not a GPU replacement — a low-power neuromorphic edge layer for defense, SIGINT, ISR, sensors and physical AI. The cluster trains intelligence. The edge has to detect and act locally. That is where $BRN.AX / $BRCHF could matter @BrainChip_inc $BCHPY #NVDA electronicsforu.com/news/new-platf…

Another strong signal for the neuromorphic thesis. Nature Materials is now highlighting high-precision memristor-based computing as a serious path for future AI efficiency. This is not directly about BrainChip, but it reinforces the same core point: AI will not scale forever on brute-force compute alone. Power, data movement, memory bottlenecks and edge inference require new architectures. BrainChip’s Akida is already positioned in that direction: neuromorphic, event-based, ultra-low-power inference for edge AI. The cluster trains intelligence. The edge and new architectures deploy it efficiently. $BRN.AX $BRCHF @BrainChip_inc

I j came across BrainChip about 6 months ago, and once I understood the technology and the problem they are trying to solve, I became very interested. To me, $BRN.AX is not just another AI name. The real opportunity is ultra-low-power inference at the edge — sensors, robotics, automotive, industrial systems, defense, satellites and physical AI. I know it may still take time, but if they execute, I believe BrainChip can become a very successful company. My personal goal is to keep accumulating and eventually reach at least 750K shares.

This is exactly the BrainChip thesis. 🚀🚀🚀🚀🚀🚀🚀🚀🚀 Neuromorphic computing does not need to replace GPUs to matter. It only needs to solve the workloads where GPUs are inefficient: always-on sensors, real-time edge inference, anomaly detection, robotics, satellites, industrial systems and physical AI. The AI bottleneck is moving from only “more compute” to power, heat, latency and data movement. That is where Akida could become important: a complementary edge AI layer for ultra-low-power, event-based inference close to the sensor. The cluster trains intelligence. The edge deploys it efficiently. $BRCHF $BRN.AX

@ACInvestorBlog Técnico se salvou ! Tirou CR7 …. Lucky 🍀 that worked out good 👍🏻🚀

If the next bottleneck is moving AI from data centers into the real world — robots, drones, vehicles, satellites, defense systems, sensors and industrial machines — then the constraint is not only compute. It becomes power, heat, latency, bandwidth and always-on inference at the edge. That is where @BrainChip_inc BrainChip’s Akida is interesting. I don’t see $BRCHF / $BRN.AX as a GPU replacement. I see it as a possible edge AI co-processor layer — neuromorphic, event-driven, ultra-low-power inference close to the sensor. The trillion-dollar clusters train the intelligence. Physical AI needs to deploy that intelligence everywhere, continuously and efficiently. That second bottleneck is where BrainChip could matter.

If the next bottleneck is moving AI from data centers into the real world — robots, drones, vehicles, satellites, defense systems, sensors and industrial machines — then the constraint is not only compute. It becomes power, heat, latency, bandwidth and always-on inference at the edge. That is where @BrainChip_inc BrainChip’s Akida is interesting. I don’t see $BRCHF / $BRN.AX as a GPU replacement. I see it as a possible edge AI co-processor layer — neuromorphic, event-driven, ultra-low-power inference close to the sensor. The trillion-dollar clusters train the intelligence. Physical AI needs to deploy that intelligence everywhere, continuously and efficiently. That second bottleneck is where BrainChip could matter.

@OnlyTrades365 I have the feeling 390 is the top for now . We will see 350 before continuing.

@HelloBenWhite @TheCon2024 95th minute or not, that’s when Brazil becomes dangerous. No Neymar-only story, no one-player hype, no spotlight on just 1 or 2 stars. When Brazil grows inside the tournament and everyone starts feeling like they can make history, that team becomes a serious problem. 🇧🇷

@footballbears2 But the podcast is from 2021. Should have disclosed at this point

@leopoldasch @elonmusk Leopold, your Situational Awareness framework correctly focuses on the real bottlenecks behind AGI: compute, power, data centers, infrastructure buildout, and the ability to scale intelligence fast enough. One company I think deserves a serious look through that same bottleneck lens is BrainChip. Not because it replaces NVIDIA, AMD, or the trillion-dollar cluster story. It does not. But because after frontier models are trained, the next constraint becomes deployment: how do you push intelligence into the physical world — robots, drones, vehicles, cameras, industrial sensors, satellites, defense systems, wearables, and autonomous devices — without sending everything back to the cloud and without burning data-center-level power at the edge? That is a different bottleneck: joules-per-inference, latency, thermal limits, privacy, sensor bandwidth, and always-on autonomy. BrainChip’s Akida is built for that layer. Neuromorphic, event-based inference closer to the sensor, designed for ultra-low-power edge AI, with on-chip learning/adaptation capabilities. In a world where intelligence moves from centralized training clusters into billions of physical endpoints, this becomes strategically important. The question is not: “Can BrainChip replace GPUs?” The better question is: “What silicon handles the billions of small, continuous, always-on AI decisions that GPUs are too power-hungry, too hot, or too expensive to handle locally?” That is where BrainChip could matter. If AGI creates massive demand for centralized compute, it should also create massive demand for efficient edge inference. The cluster builds the intelligence. The edge deploys it into reality. BrainChip is one of the few public companies directly positioned around that second bottleneck. Worth a serious look, especially if the next phase of AI is not only bigger models, but intelligence everywhere.

@leopoldasch Leopold, your Situational Awareness framework correctly focuses on the real bottlenecks behind AGI: compute, power, data centers, infrastructure buildout, and the ability to scale intelligence fast enough. One company I think deserves a serious look through that same bottleneck lens is BrainChip. Not because it replaces NVIDIA, AMD, or the trillion-dollar cluster story. It does not. But because after frontier models are trained, the next constraint becomes deployment: how do you push intelligence into the physical world — robots, drones, vehicles, cameras, industrial sensors, satellites, defense systems, wearables, and autonomous devices — without sending everything back to the cloud and without burning data-center-level power at the edge? That is a different bottleneck: joules-per-inference, latency, thermal limits, privacy, sensor bandwidth, and always-on autonomy. BrainChip’s Akida is built for that layer. Neuromorphic, event-based inference closer to the sensor, designed for ultra-low-power edge AI, with on-chip learning/adaptation capabilities. In a world where intelligence moves from centralized training clusters into billions of physical endpoints, this becomes strategically important. The question is not: “Can BrainChip replace GPUs?” The better question is: “What silicon handles the billions of small, continuous, always-on AI decisions that GPUs are too power-hungry, too hot, or too expensive to handle locally?” That is where BrainChip could matter. If AGI creates massive demand for centralized compute, it should also create massive demand for efficient edge inference. The cluster builds the intelligence. The edge deploys it into reality. BrainChip is one of the few public companies directly positioned around that second bottleneck. Worth a serious look, especially if the next phase of AI is not only bigger models, but intelligence everywhere.

Great framework. I would add one more important layer inside the edge compute bottleneck: ultra-low-power neuromorphic inference. As AI moves from data centers into robots, vehicles, cameras, sensors, industrial systems and physical devices, the constraint will not only be CPU/GPU availability. It will also be power, heat, latency and privacy at the device level. That is where BrainChip’s Akida becomes very interesting. It is not trying to replace NVIDIA or AMD; it can complement them by offloading always-on perception, sensor fusion and anomaly detection workloads with much lower power consumption. If AI is going everywhere, efficient edge AI silicon becomes a bottleneck too. BrainChip could be one of the companies positioned for that next layer. @StockSavvyShay @BrainChip_inc

@vandy_trades Coming along! Almost decision time on $SPY @vandy_trades

Setup alert on $NVDA! 🚨 As shown in the 30m chart, the price has reached a massive decision zone around $200. 📈 Bull Case: Triple Bottom confirmation ("Bottom 3") eyeing the $230 target. 📉 Bear Case: Losing support validates the Rising Wedge breakdown toward the $190 zone. Make-or-break moment for the next few days. Keep your eyes on the trigger! 📈📉 #NVDA #Trading #PriceAction $SPY $QQQ

@OnlyTrades365 Yes ! I did !!

@MichaelPBento So far …. Has been reaching the target. 🎯 let’s if we bounce or going down . @MichaelPBento @market_sleuth

Taco 🌮 Tuesday or : $SPY weekly roadmap: Watching the 750 resistance / dark pool zone closely. As long as SPY stays below it, I’m looking for a possible pullback into the 738–732 bounce area. A break below 738 could open the door toward the 720–724 gap Reclaim 750, and the bearish setup gets invalidated. Patience. Levels first. #SPY #SPX