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We keep reading scary reports of swarms of AI agents breaking into computer systems and doing other untoward and perhaps illegal things. To what degree do these agents influence each other? And is there anything we can learn from human behavior that would help mitigate these problems? That’s the heady backstory behind [episode 46](https://www.tractionheroes.com/2439976/episodes/19905275-tribal-psychology) of [_Traction Heroes_](https://www.tractionheroes.com/). Harry brought a short reading from David McRaney’s [_How Minds Change_](https://www.amazon.com/How-Minds-Change-Surprising-Persuasion-ebook/dp/B093R2CP2V/) about the impact of our groups on our thinking and behavior. Here’s the key fragment: > the latest evidence coming out of social science is clear. Humans value being good members of their groups much more than they value being right. So much so that as long as the group satisfies those needs, we will choose to be wrong if it keeps us in good standing with our peers. We never lack evidence for this: just tune in to the news. People do stupid things to remain in good standing with their milieu _all the time_. (I know I have!) And apparently, so do AIs. (Although as I pointed out in the show, we mustn’t anthropomorphize technology.) But more importantly, knowing about the perils of tribal psychology can help us be more effective when working in groups — and therefore, to gain traction. [_Traction Heroes episode 46: Tribal Psychology_](https://www.tractionheroes.com/2439976/episodes/19905275-tribal-psychology)
Are you more of an analytical or a holistic thinker? The answer might depend on the culture in which you were raised — and will impact your ability to gain traction. In episode 44 of Traction Heroes, Harry read a short segment from Joseph Henrich’s The WEIRDest People in the World, which argues that people in “WEIRD” societies — Western, Educated, Industrialized, Rich, and Democratic — tend to think more analytically than holistically. I haven’t read the book, but do believe our culture influences our worldview — including how we think of timeframes and outcomes. If we’re driven by quarterly results, we’ll have a very different orientation than if we have a 10-year outlook. Learning how culture affects our perspective changes how we understand ourselves and how we communicate with others — and both affect our ability to gain traction. Traction Heroes episode 44: WEIRD People
It’s been fifty years since information architecture’s coming out party in Philadelphia. Alas, the discipline is still widely misunderstood. The main obstacle? The very thing that brought it to many people’s attention: the World Wide Web. I’d venture most people who’ve heard the phrase ‘information architecture’ have done so in the context of designing website navigation systems. That’s understandable: The web is a huge deal and IA was exactly what it needed early in its development. But IA has more to offer than its contributions to UX design. To understand why, consider its ultimate purpose. This is how I describe it: The purpose of information architecture is increasing agency by making systems more legible. Let’s unpack this statement. The word agency is loaded now. We talk of agentic systems to mean those powered by AI agents. But here, I mean ‘agency’ in its original sense: giving an actor (human or otherwise) scope to decide and act independently. Systems are the scope we’re acting on. What is a system? A collection of parts that relate to one another in particular ways so that the whole can serve a purpose. A website is a system. So is a business. Systems can have subsystems. Many websites are subsystems in service of broader business systems, which are in service of broader social systems. Architects are always aware of the broader context; we operate holistically. As Eliel Saarinen put it, Always design a thing by considering it in its next larger context — a chair in a room, a room in a house, a house in an environment, an environment in a city plan. IA makes systems more legible. That is, the actor who’ll use the system will be better able understand what to do with it to accomplish their goals. Think back to a time when you had to use a complex, unfamiliar product. You scanned its user interface for recognizable labels and symbols, looking for the smooth handle. If you’re like me, the system’s illegibility led you to YouTube, the web, or an LLM for an explanation. IA fixes that — or at least aims to make the process less onerous. To recap, the ultimate purpose of IA is increasing agency — enabling actors to make good choices — by making systems more legible. Let’s stress-test this claim against some real-world applications: A website’s navigation structure should give users recognizable labels that allow them to find their way to the part of the website that has the information they need. The user is the agent; the labels give them clear choices that allow them to make the right decisions about where to click. A spreadsheet gives an executive the information they need to understand how their part of the business is functioning. The executive doesn’t need all the data; that might drown the signal in noise. But the right data shown at the right time and place will allow them to make good business decisions. Your car’s speedometer tells you how fast you’re going, allowing you to remain compliant with traffic laws. Again, a car generates lots of data. Modern dashboards are carefully designed to put the most important front and center: speed, fuel/energy levels, etc. — ‘most important’ being the ones you need to make critical decisions. A map makes physical environments more understandable by collapsing their scale and details into a set of abstractions you can hold in your hand. Reading the map lets you make better decisions about which roads to take to get to your destination. A carefully structured prompt allows a large language model to work properly. You could point the LLM to a broad corpus, but that wouldn’t improve its performance. The point of ‘context engineering’ (which is awfully close to IA) is giving the LLM the right information at the right time with the right instructions so it produces useful outcomes. One of the most common questions I’ve gotten post-LLMs is, “Why do we need information architecture now that we have chatbots?” This is old-school thinking. “For the world wide web” was a phase of information architecture’s development — just a phase. It’s never been more important to re-embrace the discipline’s broader origins and aspirations.
Forty years ago, computer scientist Fred Brooks published a paper called No Silver Bullet: Essence and Accident in Software Engineering. As its title implies, the paper argues there are no technological shortcuts to making software radically easier, simpler, or more reliable. You may think AI is the ultimate silver bullet. It isn’t. Moore’s law was in full force in 1986. Hardware was getting more powerful, faster, and cheaper. Surely, some technology would come along to do the same for software. Brooks argued this wasn’t in the cards, since software is fundamentally different from hardware. For one thing, it’s of a different order: The essence of a software entity is a construct of interlocking concepts: data sets, relationships among data items, algorithms, and invocations of functions. This essence is abstract, in that the conceptual construct is the same under many different representations. It is nonetheless highly precise and richly detailed. Specifying, designing, and testing this construct is difficult. The challenge isn’t implementation but design: “We still make syntax errors, to be sure; but they are fuzz compared to the conceptual errors in most systems.” Technical advances usually make development easier. No Silver Bullet traces the history of time sharing, unified programming environments, and high-level languages. Object-oriented programming was a promising new technology at the time and there were even rudimentary AIs in the form of expert systems. Brooks examines them and concludes they’re not enough. Why? Because coding isn’t the hardest part of making software. Instead, the hard part is knowing what to build: The hardest single part of building a software system is deciding precisely what to build. No other part of the conceptual work is so difficult as establishing the detailed requirements, including all the interfaces to people, to machines, and to other software systems. No other part of the work so cripples the resulting system if done wrong. No other part is more difficult to rectify later. What will the system do? How will it serve strategic objectives? How will it enable better judgment and allow people to derive meaning from data? These aren’t implementation questions, they’re design questions. Somebody must define the “construct of interlocking concepts” that define the system, aiming for good fit between the system and the context it serves. LLMs can help, but they can’t replace human understanding and judgment, at least not yet. Brooks calls out four inherent properties of modern software systems: Complexity: Software systems are among the most complex human constructs. They’ve only gotten more so as computers and operating systems have grown more powerful and capable. Conformity: Software solutions must conform to the goals, needs, constraints, and interfaces of the organizations that bring them forth. This is true whether it’s bought off-the-shelf or developed bespoke. Changeability: Anything that lasts does so because it’s able to adapt to changing conditions. Software is inherently more malleable than other complex designed systems, such as buildings. Invisibility: Whereas complex physical systems (again, think of buildings) can be represented with mechanical drawings, software specs are inherently abstract. This makes them hard to design. There’s been progress in the last four decades, but these properties remain fixed. LLMs haven’t changed that. Non-deterministic components add immense complexity and unpredictability to software systems. The ease, speed, and volume of code generation make software more malleable and opaque than ever. And LLMs promise to ease bespoke development, tempting orgs away from one-size-fits-all solutions. Which is to say, LLMs haven’t changed the nature of software. Instead, they’ve made it more so. So far, the technology’s killer application is developing software: teams can now produce more software, faster. (It’s unclear yet whether it’ll ultimately be cheaper, especially when you consider maintenance costs.) What LLMs haven’t done yet is replace software wholesale, at least not for tasks that require predictable behavior. And as their true costs and constraints become evident, it’s increasingly doubtful they will. Instead, LLMs will likely become part of systems that include traditional deterministic components — both inside the systems and as part of the development process. The resulting systems will be more complex, malleable, and abstract than prior ones. They’ll also be better fit to purpose than off-the-shelf solutions. But that requires design, which remains primarily a human challenge. And it’s hard: it is really impossible for clients, even those working with software engineers, to specify completely, precisely, and correctly the exact requirements of a modern software product before having built and tried some versions of the product they are specifying. Replace “software engineers” with LLMs, and this sentence still stands. But it also hints at where LLMs come closest to being a silver bullet: in their ability to spin up rapid prototypes. Good software is grown, not built. That is, it evolves from an initial core to a more complex system through an organic approach that respects Gall’s law: The building metaphor has outlived its usefulness. It is time to change again. If, as I believe, the conceptual structures we construct today are too complicated to be accurately specified in advance, and too complex to be built faultlessly, then we must take a radically different approach. Let us turn to nature and study complexity in living things, instead of just the dead works of man. Here we find constructs whose complexities thrill us with awe. The brain alone is intricate beyond mapping, powerful beyond imitation, rich in diversity, self-protecting, and self-renewing. The secret is that it is grown, not built. So it must be with our software systems. What was true then is true now: technology moves the bottleneck from production to orientation. LLMs make coding easier, much like high-level languages, IDEs, and compilers did in the past. But without shared models, structured context, feedback loops, governance, and clear interfaces, they won’t provide the results leaders expect. As always, how to build gets easier — knowing what to build doesn’t. AI can help with that too — but it needs steering. The question isn’t “Which systems can we replace with AI?” Rather, it’s “How can AI help us grow systems that better fit our needs?” The answer will consider AI as a system component and a production tool. But forty years on, there’s still no silver bullet — just better ways to find good fit, faster.
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By a mile, the iPhone Duo has garnered more questions about what I think about it than any other phone I’ve ever had. It’s clearly a phone that has people intrigued, so I thought I’d jot down some loose, unorganized thoughts about what I think about it after about ten days. If I could sum all my thoughts up into one word, it’d simply be “fun”. I think for the vast majority of people, myself included, the iPhone 18 Pro is a more practical phone that through scientific analysis you’d likely even find a better phone. Even if they were priced the same, its package is more traditionally comfortable and ergonomic, with a better quality main camera, a helpful telephoto camera, a screen that’s more practical to use more of the time, a better biometric system (Face ID versus Touch ID) and a more established software experience. But damn if the iPhone Duo isn’t fun. Phones have been downright boring for so long. Incredible technology, lovely apps, but the hardware has been a bit slow moving since the iPhone X. Maybe that’s why I jumped onto the iPhone Air so readily. Even that slight departure from “black slab of glass” to “a dramatically thinner black slab of glass” was exciting versus what we’ve grown accustomed to. So I pick up the Duo, and it’s kinda just exciting to hold a phone for the first time in ages. Maybe that’s a honeymoon period and after a few months I’ll be over it. Maybe everyone who tries a folding phone for the first time experiences this. And to be clear, it’s not just all raw novelty. The fun aspect is fueled by having an absolutely incredible inner screen that fundamentally transforms what a phone is and can be. In addition, support the Apple Pencil has turned out to be phenomenally handy for me to be able to jot down quick thoughts and notes. For downsides, Touch ID is objectively worse than Face ID, but honestly, I don’t mind it as much as I expected I would. The smaller camera sensor than the Pro series phones is still decidedly great (rumors were it was a 17e size which was not correct, it’s the size of the normal 17’s). Software support gets better by the day, though iOS on the Duo is still decidedly a bit buggy at times. Other random thoughts Touch ID is objectively worse than Face ID, but honestly, I don’t mind it as much as I expected I would, previewing notifications is where I miss it the most After getting used to having a single speaker in the iPhone Air for a year, having stereo (not to mention great sounding) speakers is amazing The FaceTime camera on the inside is indeed not great for selfies/photography but honestly perfectly acceptable for FaceTime, and I’d definitely prefer this solution over a cutout on the inside so I’m happy! It definitely has a heavy feel to it, but it’s nothing honestly that bothersome A lot of the reviews said something to the effect of “the crease is practically invisible” and while I don’t really mind it or anything I definitely would disagree with that statement, you can see it a fair bit even with light content The hinge is a bit harder to open than I expected, I’ve found the secret is don’t pry from the middle of the side, instead put a thumb near the top and bottom (also helps prevent pressing Camera Control instead which I was doing all the time). I imagine this is necessary to have it be able to stop at arbitrary angles once open (which I do like) and hopefully it loosens up a bit with more use, but playing with Android foldables at Best Buy I did find their hinge easier to open I am really split on the inner, nano texture screen. It definitely helps reflections, has a neat look to it, and is super nice to draw on with the pencil, but it’s significantly less vibrant than the outer display which to my eyes looks a fair bit nicer. I think they made the right decision ultimately but it’s definitely not a zero-compromise solution. I was considering nano texture on my next MacBook but after this I think I’ll stick with glossy. My glass half full take with the Duo for now though is that we get two really cool screens to play with The outer screen is awesome for taking photos, unlike taller iPhones, your viewfinder fills the screen completely so it’s the largest feeling viewfinder I’ve used on a phone I miss the action button, nice little launcher for ChatGPT/Claude depending on which month it is Battery life is downright phenomenal versus my iPhone Air and 15 Pro (two devices admittedly not known for having great battery life) So, I don’t think the Duo is for most people who either don’t care much about phones, or care a lot about having the perfect phone, but for a decent amount of people in the middle I think the Duo represents a fundamentally different experience, that’s a ton of fun, and I’m excited to be excited about a phone again. (But, Apple, please figure out a way to add a telephoto lens.)
Going through a collection of old parts, CuriousMarc encountered some ICs that he couldn't identify. He opened up one of these ICs, and I studied it under the microscope. After some reverse engineering, I determined that it was a logic chip that implemented four NAND gates using old-fashioned diode-transistor logic (DTL).1 The chip turned out to be the ST680A, a member of the Signetics 600 series of chips from 1965. In this article, I discuss the circuitry in this chip and how diode-transistor logic works. The die of the ST680A under the microscope. Integrated circuits can be packaged in many ways. One of the most popular is the plastic DIP (Dual In-line Package), a black rectangle of epoxy with two rows of metal pins. This type of package was invented by Signetics: "Introduced in late 1965, the 600 series was the first integrated circuit family to be offered in a solid, plastic, dual in-line package (DIP)." Earlier integrated circuits were packaged in round metal cans or in small flat-packs. In 1965, engineers at Fairchild invented the DIP, which had two rows of pins on 0.1" spacing. Unlike the surface-mounted flat-pack, the DIP had pins that were inserted into holes, making manual or automated assembly quick and easy. Because the pins of a DIP were on a regular grid, rather than the circular pattern of a metal can, circuit board design and manufacturing were also easier (details). The first DIP chips had ceramic packages. The plastic DIP, introduced by Signetics, was much cheaper and became very popular. An advertisement for the Signetics 600 series. From Electronics, March 21, 1966. The plastic DIP is less popular with me, however, because it is harder to open. With a ceramic package, I can simply tap it with a chisel and pop it open, but that doesn't work with epoxy packages. An epoxy package can be dissolved with nitric or sulfuric acid, but I try to avoid dangerous acids. Instead, CuriousMarc used Antoine's hot air decapping method. Unfortunately, the hot air technique doesn't always work. In this case, tiny bits of epoxy were stuck to the die, obscuring the circuitry. I used a few drops of boiling sulfuric acid to clean the die. (I'm okay with sulfuric acid in tiny quantities.) The die on its metal frame. The photo above shows the silicon die on its metal frame, the lead frame. The golden blobs are where the bond wires were attached to the die. You can see the doped silicon regions that form diodes and bipolar NPN transistors, as well as 16 resistors in the center, forming serpentine paths. Most chips have a passivation layer—a thin layer of silicon dioxide—over the chip. Inconveniently, this old chip didn't have a passivation layer, so the acid also stripped off the metal layer. (A few remnants are visible in the upper left.) Without the metal layer, I had to guess at how the components were connected to form logic gates. The die has very faint color differences where the silicon was protected by the metal, so that helped. Eventually, I came up with the diagram below, showing how the metal layer was probably configured. This wiring reveals that the chip implements four DTL NAND gates, which I'll describe in more detail below. One puzzle was that the chip has two unused diodes, indicated with question marks below. The metal layer for a quad NAND chip. I desaturated the image to make the labels clearer. Transistors, diodes, and resistors In this section, I'll explain how the patterns in the image correspond to components of the circuit. The silicon die is doped with various impurities to form regions with different electrical characteristics. These regions appear red or green under the microscope due to thin-film effects. As a result, the chip's transistors, diodes, and resistors are visible. The chip is made from bipolar NPN transistors, which have a completely different construction from the MOS transistors in modern ICs. In an NPN transistor, the current from the collector to the emitter is controlled by the current through the base. The collector is a large region of N-type silicon, silicon that is doped with atoms such as phosphorus or arsenic. Since these elements have one more valence electron than silicon, they give the silicon more negative charge carriers, so it is called N for negative. The N-type silicon happens to appear reddish. On top of the collector, the base is a thin region of P-type silicon, which appears green. This silicon has been doped with atoms such as boron or gallium, which have one fewer valence electron than silicon. The missing electrons form "holes", which act as positive charge carriers. Finally, the emitter is N-type silicon, on top of the base layer. These layers work together through semiconductor magic, amplifying the base current. Two NPN transistors on the chip. The second transistor is a larger output transistor. The chip's output transistors are slightly larger so they can sink more current. In the transistor on the right above, note that the collector contact wraps around three sides of the transistor. The emitter is also larger, with an L-shaped contact. According to the datasheet, each output transistor can sink 2.5 mA of current. The chip uses many diodes since it is diode-transistor logic. The structure of a diode is similar to a transistor, but without the emitter. In a diode, current flows from the cathode (P-type silicon) to the anode (N-type). A diode and a resistor on the chip. The chip uses 16 resistors, located in the middle of the die. A resistor is constructed from a long, thin region of doped silicon. Since silicon has moderately high resistance, a long strip of silicon acts as a resistor. Most of the resistors have a serpentine layout to fit a long resistor into a smaller area. These resistors consist of P-type silicon (green) in a "tub" of N-type silicon (red). The tub is connected to the positive voltage supply; since current doesn't flow from N-type silicon to P-type silicon, this isolates the resistors from each other. Operation of a DTL gate Logic gates can be created in many ways. The earliest integrated circuits (such as the ones in the Apollo Guidance Computer) used Resistor-Transistor Logic (RTL), where logic gates were constructed from resistors and transistors. In March 1962, Signetics announced the first family of commercial Diode-Transistor Logic (DTL) integrated circuits (details). By using diodes on the inputs instead of resistors, DTL provided better performance than RTL. However, DTL soon became overshadowed by TTL (Transistor-Transistor Logic), which was faster and used less power. (If you built digital circuits a few decades ago, you're probably familiar with the popular 7400 series of TTL chips.) The diagram below shows how the DTL NAND gate works. If both inputs are high, the current from the first resistor flows through the base of the first transistor, turning it on. The transistor amplifies this current, passing a much larger current from the transistor's collector to the transistor's emitter. This current goes through the base of the second transistor, which turns on the second transistor. The second transistor passes an even larger current from the output pin to ground, pulling the output pin low. The DTL NAND gate with both inputs high. The schematic is from the datasheet. This circuit is considerably more complicated than the DTL gate on Wikipedia. But if one of the input pins is pulled low, the current from the resistor goes through the diode instead of the transistor. The first transistor turns off, and without a base current, the second transistor turns off. The third resistor pulls the output high. Thus, the output is low only if both inputs are high: a NAND gate. The DTL NAND gate with an input low. I'll point out a few features of this circuit. The diodes on the inputs ensure that one input doesn't affect the other input. The diode in the middle raises the voltage on the first transistor's emitter, so it requires more voltage to turn on, reducing the sensitivity to noise. The output uses a resistor to pull the output high; more advanced circuits use another transistor. When the output is low, the third resistor is still "trying" to pull the output high, which is wasting power. Moreover, the resistor limits the output current, so the high output is fairly weak. In some DTL families, you can get gates with different values for the output resistor, so you can select the tradeoff between wasting power and having an output that is too weak. If you've used TTL integrated circuits from the 1970s, you know that chips usually have power and ground on diagonal pins: ground on pin 7 and power on pin 14. (Putting power and ground diagonally opposite both helps die layout and circuit board layout.) Much to my surprise, I found that the Signetics chips predate this standard and have ground on pin 1 and power on pin 8: still diagonal, but the wrong diagonal! The diagram below shows how the four NAND gates are wired in the chip. Pinout of the 680A quad NAND gate. From the databook. Three chips from one die When I examined the die under the microscope, I figured out the circuit, but I was puzzled by two extra diodes, as if two NAND gates had a third input that wasn't used. The explanation is that Signetics used the same silicon layout for at least three different chips, applying a different metal layer for different functionality. This saved money since it's much cheaper to produce a single optical mask for the metal layer than a complete set of new masks. Signetics sold semi-custom ICs to customers, using an approach called variFEBS (Variable Functional Electronics Blocks). This approach used a custom metal layer on top of fixed silicon, providing custom circuits without the cost of a full mask set. These circuits didn't sell well, but Signetics used the same approach for standard ICs. Pinout of the 670A triple NAND gate and the 616A quad NAND gate. All three chips have some similarities, such as using pins 2 and 14 as outputs. From the databook. One IC with the same silicon as the quad NAND gate is the 670A chip, which has three NAND gates, each with three inputs (above left). The diagram below shows how the silicon can be wired to provide this functionality. The hypothesized metal layer for the 670A triple-NAND gate. The layout for the triple chip above is a bit tricky because the top gate has inputs on both sides of the power (Vcc) pin. This creates a layout problem because the power and ground metal runs vertically to the resistors, blocking metal wiring from one side to the other. The solution is a "crossunder", a region of silicon that allows a signal to pass under the metal layer, connecting the inputs on the left to the rest of the circuitry on the right. This crossunder is visible near the top of the chip. Another chip with the same layout is the 616A dual NAND gate, with five inputs on each gate. Since this chip has 10 inputs in total (compared to 8 on the quad gate), this chip uses the two "extra" diodes that I spotted. If that isn't enough inputs, each NAND gate has an expansion input; by connecting external diodes to the expansion input, the gate can be made as large as desired. The hypothesized metal layer for the 616A dual-NAND chip. Some history The history of Signetics is closely tied to Fairchild. The story of Fairchild's founding is well known: in 1957, eight engineers—the Traitorous Eight—left Shockley Semiconductor and created Fairchild Semiconductor. Hundreds of companies (the "Fairchildren") were started by engineers who left Fairchild. Signetics was one of those companies, formed in 1961 and said to be the first company to exclusively design and manufacture integrated circuits.2 Signetics announced its first product in 1962, a NAND gate that cost $125 (over $1000 in current dollars).3 Not surprisingly, sales were weak. Signetics soon ran short of money, so in 1962, Corning Glass invested $1.7 million for 51% ownership of Signetics. Corning Glass may seem like a strange investor, but Corning was more of a high-tech company than you'd expect, making components such as resistors and glass capacitors, and later creating fiber optics. Moreover, in the early 1960s, integrated circuits were the hot area (much like AI now), and everyone wanted to get involved. In 1963, the Department of Defense pushed for contractors to use integrated circuits in new products. Riding this wave, Signetics became the leading producer of integrated circuits.4 However, Fairchild soon introduced a series of DTL chips that competed with Signetics. The Fairchild 930 series was faster than the Signetics chips and less than half the price, so Fairchild took much of the market from Signetics and dominated the IC market. Signetics also produced analog integrated circuits. Signetics is probably best known for creating the 555 timer chip in 1970, said to be the world's most popular IC, with billions sold.5 Signetics was acquired by Philips in 1975, and then Philips Semiconductors was spun off as NXP (Next exPerience) in 2006. NXP is currently the #10 semiconductor manufacturer worldwide, so the heritage of Signetics lives on. For more, follow me on Bluesky (@righto.com), Mastodon (@[email protected]), or RSS. Thanks to CuriousMarc for providing the IC and thanks to TubeTime for finding the databook. AI statement: Despite the presence of the em dash, no AI was used in the writing of this article (details). Notes and references After I had reverse-engineered the chip, TubeTime found datasheets for it, making my reverse engineering unnecessary. (See the Signetics Condensed Catalog 1969, Utilogic II Databook 1971, and Utilogic 2 Databook 1972.) We must have been unlucky with our original web searches since we didn't find anything. But this way you get to see what's inside the chip :-) The Signetics ST680A IC in its package. The large circular depression marks pin 1. From the date code, this chip is from 1971. ↩ For more Signetics history, see Signetics Founding History, written by Lionel Kattner, a co-founder of Signetics. He states that the company name was inspired by the words "signals" and "networks", as well as the high-tech suffix "-netics". ↩ See History of Semiconductor Engineering, page 156. ↩ The book Making Silicon Valley describes the history of Signetics. It has a negative view of the Corning investment, saying that Signetics executives "Van Poppelen and Kattner dissented on the grounds that Corning was not a good fit for an integrated circuit company. In their view, Corning was a staid Eastern firm, ignorant about semiconductors and antithetic to the ways and culture of the electronics industry on the West Coast. Their view later proved to be correct." ↩ The 555 timer was created by analog IC wizard Hans Camenzind. If you're interested in analog circuits, you should read his book Designing Analog Chips, which you can download for free. ↩
A great deal of effort has gone into understanding the regulatory burden on housing construction, and the extent to which it contributes to high housing prices.
