Tim Canham

🧠 Memory isn't an add-on—it's the core of any truly intelligent agent. In my upcoming blog, I dive into the actual implementation of a layered memory architecture for AI agents—designed to handle fast recall, session continuity, and long-term knowledge grounding using a triad of: 🔹 L1 – In-Memory Cache (Active Context) 🔹 L2 – Vector DB (Session Memory) 🔹 L3 – Graph DB (Knowledge Memory) This isn't just theoretical. I’ve put this into practice with real agent frameworks and explored how memory impacts performance, continuity, and contextual reasoning. From agent personalization to retrieval-aware decision making, memory is what makes agents feel less like tools—and more like intelligent collaborators. 🚀 The blog walks through each layer, how to wire them up, and the tangible benefits they unlock when combined. Stay Tuned #AI #GenAI #RAG #AgenticRAG #AIAgents

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𝗪𝗵𝗮𝘁 𝗶𝗳 𝗮𝗻 𝗙𝗣𝗚𝗔 𝗰𝗼𝘂𝗹𝗱 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗰𝗹𝗲𝗮𝗻 𝗥𝗙 𝘀𝗶𝗴𝗻𝗮𝗹𝘀 𝘂𝘀𝗶𝗻𝗴 𝗼𝗻𝗹𝘆 𝗼𝗻𝗲 𝗯𝗶𝘁? Sigma-delta (ΣΔ) converters rely on noise shaping to push quantization noise outside the band of interest, enabling very high in-band SNR even with a 1-bit quantizer. The ΣΔ loop integrates and feeds back the quantization error, producing a flat Signal Transfer Function (STF) and a Noise Transfer Function (NTF) that shifts noise to higher frequencies. While effective at baseband, RF systems require a clean spectral region around a carrier, not at DC. 𝗕𝗮𝗻𝗱-𝗣𝗮𝘀𝘀 𝗦𝗶𝗴𝗺𝗮-𝗗𝗲𝗹𝘁𝗮 (BP-ΣΔ) modifies the loop so the NTF creates a notch at a center frequency “f0”, pushing noise away from that RF band. A 𝟭-𝗯𝗶𝘁 BP-ΣΔ DAC can directly synthesize an RF signal at f0 using standard 𝗙𝗣𝗚𝗔 𝗜/𝗢, 𝗦𝗘𝗥𝗗𝗘𝗦 or 𝗠𝗚𝗧 𝗹𝗶𝗻𝗸𝘀, providing high linearity in a narrow channel. This enables all-digital transmitters implemented entirely in logic, without multipliers or wide datapaths, and even allows intentional aliasing to place spectral replicas in the desired band. For modern RF architectures where size, weight, power, or cost are critical, a BP-ΣΔ modulator can turn digital logic into a high-quality RF source. Devices like the AD9874 from Analog Devices use a BP-ΣΔ ADC for IF digitization, while other works use 1-bit BP-ΣΔ DACs to implement 𝗳𝘂𝗹𝗹𝘆 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗺𝗶𝘁𝘁𝗲𝗿𝘀 that drive switched-mode RF power amplifiers with high efficiency. The attached figure illustrates a practical case: a fourth-order 1-bit BP-ΣΔ DAC implemented in an FPGA, the corresponding MathWorks MATLAB simulation of its digital output spectrum for a tone centered at a sampling-rate fraction of fs/4, and a possible RF transmit chain including switched-mode amplification and an output filter. The spectrum clearly shows how the FPGA output pushes the quantization noise away from the useful band around fs/4. For readers interested in 𝗳𝘂𝗹𝗹𝘆 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝗥𝗙 𝘁𝗿𝗮𝗻𝘀𝗺𝗶𝘁𝘁𝗲𝗿𝘀 using 𝟭-𝗯𝗶𝘁 𝗗𝗔𝗖𝘀, the following references describe implementations where BP-ΣΔ modulators are integrated inside FPGAs, often using Multi-Gigabit Transceivers (MGT) to generate signals efficiently at GHz frequencies: 𝗔𝗻 𝗙𝗣𝗚𝗔-𝗯𝗮𝘀𝗲𝗱 𝗠𝘂𝗹𝘁𝗶-𝗹𝗲𝘃𝗲𝗹 𝗔𝗹𝗹-𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗺𝗶𝘁𝘁𝗲𝗿 𝘄𝗶𝘁𝗵 𝟭.𝟮𝟱 𝗚𝗛𝘇 𝗼𝗳 𝗕𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵 https://lnkd.in/dFGj4yKP 𝗚𝗶𝗴𝗮𝘀𝗮𝗺𝗽𝗹𝗲 𝗧𝗶𝗺𝗲-𝗜𝗻𝘁𝗲𝗿𝗹𝗲𝗮𝘃𝗲𝗱 𝗗𝗲𝗹𝘁𝗮-𝗦𝗶𝗴𝗺𝗮 𝗠𝗼𝗱𝘂𝗹𝗮𝘁𝗼𝗿 𝗳𝗼𝗿 𝗙𝗣𝗚𝗔-𝗯𝗮𝘀𝗲𝗱 𝗔𝗹𝗹-𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗧𝗿𝗮𝗻𝘀𝗺𝗶𝘁𝘁𝗲𝗿𝘀 https://lnkd.in/dz3p5SQz 𝗔 𝟮𝟲𝗚𝗛𝘇-𝗯𝗮𝗻𝗱 𝗜𝗺𝗮𝗴𝗲 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗺𝗲𝗻𝘁 𝗧𝘆𝗽𝗲 𝟭-𝗕𝗶𝘁 𝗗𝗔𝗖 𝗳𝗼𝗿 𝗗𝗶𝗿𝗲𝗰𝘁 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗥𝗙 𝗠𝗼𝗱𝘂𝗹𝗮𝘁𝗼𝗿 https://lnkd.in/dsi-K2Qw #FPGA #DSP #RF #Matlab #Wireless

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My latest Substack is now live: Slacker Index: Why vertical integration is key for rapid hardware development In part 2 of a series, I break down what Slacker Index is and why optimizing lead times is so critical for enabling rapid, agile hardware development. https://lnkd.in/gwZczwdf

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Based on the response to the dance, I wanted to share a little behind-the-scenes moment from just a couple hours before showtime. This was the first full run with both robots performing together. Until then, I had only tested sections of the routine with Rivet. Astro had never attempted any choreography before. Same moves, same commands… but very different results. Rivet handled it without issue. Astro collapsed. Turns out he was still compensating for a payload that had been removed earlier. Once we updated the config, he was dancing like a pro. If there’s one thing programming a robot to dance teaches you, it’s how complex its normal motion really is. From CG shifts to footstep sequencing, body rotation, and timing—choreography mode strips away the some of the safety layers and gives you a whole new respect for how these robots move… and even more for what living things can do. #Robotics #EngineeringFails #BostonDynamics #MotionControl #RobotChoreography

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Digital Twins I was slightly exasperated to hear from three people this week who disagreed with the name "digital twins." I seriously think by now that train has left the station. There are already more than 20,000 papers on digital twins on Google Scholar in 2025. My colleague Mike Grieves and I have battled these misconceptions since we adopted the name "digital twin" in 2010. One of the early names for the internet was "Intergalactic Computer Network." Similarly, an early name for cybersecurity was "Information Security (InfoSec)." In the early days of the digital twin concept, alternative opinions included digital systems model and even virtual clone. This week, somebody offered a mini-dissertation on the name "phygital twins" to replace it. Oh, good grief! Humpty Dumpty famously stated, "When I use a word, it means just what I choose it to mean—neither more nor less." The widespread adoption by the broad community has voted and chosen the meaning of our term. What’s in a name? Sometimes, it is simply a perfect fit, and that is precisely the case with "digital twins."

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"Be not the first by whom the new are tried, Nor yet the last to lay the old aside." -Alexander Pope I haven't commented much about AI here, but the quote above pretty much sums up my current feelings on the subject. I've watched curmudgeons say all AI is terrible or dangerous and just about shouldn't be used at all. On the other hand, I've seen AI fan boys act like anything not AI is a waste of time. Truth is somewhere in the middle, methinks. AI is not (or shouldn't be) an end to itself. It's a tool. A potentially powerful tool, yes. But just a tool -- and one among many. As another saying goes, "He who is good with a hammer tends to think everything is a nail". No tool is optimal for every need. The part we don't seem to have settled on is where exactly AI fits in our respective toolbags. To that end, I'm glad we have the early adopters trying to stretch it and break it to see what it's really good for. Once a consistent path emerges, I'm happy to travel it too. Use whatever tool seems most appropriate to you for your task. But, if your work affects something like flight safety, you own your work output regardless of tool used. In a post-incident review, it won't be acceptable to say "But, but, but... ChatGPT (or Copilot, Claude, Galaxy, etc.) said...."

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