Saturday, November 7, 2026

Colonizing dead worlds

Red dwarfs make up about 75 percent of the stars in our galaxy. These are small (with a diameter less than six-tenths that of the Sun) and cool stars, but they can host planetary systems, and planetary systems have already been discovered around many of them.

However, in terms of the existence of Earth-like worlds, these are not the most suitable candidates. Most planets located in the habitable zone of these stars are tidally locked. This means that, as they orbit their parent star, they always present the same side of their surface to that star. The Moon, for example, orbits the Earth in a similar manner.

For planets with atmospheres, this can cause constant, turbulent airflow as temperature differences are balanced between the perpetually sunlit, heated side of the planet and the far side, which remains cool.

Due to their tidally locked rotation, these planets may also fail to develop a sufficiently strong magnetic field to protect any potential life from the star’s harmful radiation.

From the perspective of human habitability, therefore, these planets—even if they orbit a star within the habitable zone and meet other conditions—may not be suitable candidates for potential colonization.


Advantages of Red Dwarf Planetary Systems

On the other hand, planetary systems around red dwarfs also have advantages that could be leveraged as humanity continues to expand into space.

If we look at some of the already known planetary systems around stars of this type, we can see that the planets’ orbits are very close together.

For example, three planets (e, b, and c) have now been confirmed around the star Gliese 581, at distances of approximately 4.2, 6.1, and 10.8 million kilometers from the star. The distances between the orbits of these confirmed planets are therefore minimal compared to the distances between the planets in our Solar System (58 million kilometers from the Sun for Mercury, 108 million kilometers for Venus, and 150 million kilometers for Earth).

As a result, these planets are more easily accessible to one another, and travel between them takes a minimum amount of time.

The smaller size of these systems may also mean that remnants from the period when the entire system formed—such as comet nuclei (analogous to the Kuiper Belt or the Oort Cloud in our Solar System)—are located closer by, making them suitable for potential utilization.


Dead Worlds

From the perspective of human expansion, planets whose characteristics differ from those of Earth may be of interest. More specifically, planets more similar to our Moon or Mercury—dead worlds. These planets offer many advantages for extended stops, restocking supplies, or establishing smaller colonies (and perhaps even permanent settlements)—or even building massive generation ships.

Because one side of these planets’ surfaces is constantly exposed to stellar radiation that is not filtered by an atmosphere or magnetic field, the sunlit side may contain enormous amounts of the helium isotope He-3, which is produced there in the same way as on our Moon (where it reaches the surface through billions of years of bombardment by charged particles from the solar wind). In the future, this isotope is expected to serve as fuel for energy production through thermonuclear fusion and will most likely also be used to power spacecraft.

Another advantage of the perpetually illuminated side is, for example, the use of the star’s own radiation to generate energy — thanks to the perpetual day on the sunlit side, there will be no fluctuations in electricity generation (such as those that can occur, for example, when using photovoltaics on the Moon’s surface—the Luna Ring project, for instance, plans to compensate for this problem by placing photovoltaic panels around the entire circumference of the Moon)—and the radiation is not attenuated by an atmosphere.

The far side, hidden in perpetual shadow, may in turn offer abundant water reserves in the form of ice. This ice could have reached this part of the planet either during the formation of the planetary system or during later comet impacts.

On planets like this, the absence of an atmosphere—which would otherwise transfer heat from the sunlit side and could cause the gradual “destruction” of accumulated ice reserves—is a huge advantage. Meteorite impacts could pose a similar problem (since there is no atmosphere to slow or dissipate them, essentially everything strikes the surface), during which the accumulated ice could be gradually removed. But how significant would these losses be in this case?

The Moon and Mercury, for example, have similar ice deposits of this kind hidden in perpetual shadow. However, these reserves are found only in a few locations near their poles. On planets with a locked rotation, these reserves can be incomparably larger due to the size of the surface area (multiplied, if we consider planets close in size to Earth) that is hidden in perpetual shadow.

The problem with the atmosphere will increase as the planets’ mass increases, since greater gravity allows lighter, “more volatile” molecules to be retained near the surface. Therefore, the most likely candidates for “dead worlds”—those with gravitational conditions suitable for long-term human habitation—will be located much closer to the star. The following principle should apply here: the closer a planet is to the star, the more certain it is that it will lack any form of atmosphere.

However, they must not be too close to the star—on the one hand, the star’s intense radiation could complicate movement around the planet, and on the other hand, in the most extreme cases, heat could be transferred to the far side, for example, via lava flows or through an atmosphere formed by vaporized material from the near side.


Colonization

From the perspective of establishing long-term colonies, we are interested in planets where the temperature on the day side does not rise to extreme levels that could interfere with, for example, the extraction of He-3 and other raw materials, or the generation of electricity. This narrows the range in which to search for such planets around red dwarfs, but on the other hand, these limits are not as restrictive as those imposed by the habitable zone.

The outer parts of the system would also be reachable more quickly from planets orbiting closer to the star. The following illustration probably probably best illustrates this point:

Credit: Geoffrey A. Landis: Colonization of Venus

As we can see, the asteroids in the asteroid belt can be reached more quickly from Venus than from Earth, thanks to Venus’s proximity to the Sun (and thus the shorter distance required). The same, of course, applies to planets orbiting other stars that are located closer to their star. However, a shorter journey does not mean a cheaper one: a planet closer to its star lies deeper in its gravitational well, so sending a probe on a trajectory to the outer part of the system requires a greater change in velocity (delta-v), and thus more energy or fuel. This is especially true for planets in tight orbits around red dwarfs.

Furthermore, these planets have relatively simple, more or less familiar environments, where experience gained from, for example, the colonization of the Moon can be put to use. In contrast, planets more similar to Earth pose a risk of greater environmental variability and potential changes, creating more obstacles during their colonization. Another risk associated with these “more habitable” planets is the possibility of extraterrestrial life, which could be threatened by the presence of Earth-based organisms—or vice versa.

These dead planets should also orbit red dwarfs that are older than approximately one billion years, since the first billion years of a red dwarf’s life tend to be quite turbulent. Frequent flares occurring on these stars during this period could cause major problems. However, there is no clear-cut boundary: flares occur on red dwarfs even billions of years after their formation, and the appropriate age therefore depends on the specific star.

As far as age is concerned, another major advantage of these stars is their estimated lifespan, which ranges from several tens of billions to trillions of years. This longevity—incomparably longer than the lifespan of stars similar to our Sun—could be of interest from the long-term perspective of humanity’s survival.

Suitable dead worlds could have a mass slightly smaller than Earth’s, and thus lower gravity, making it easier to work on the surface or to leave it (a slightly lower escape velocity; moreover, there is no need to deal with the aerodynamics of spacecraft due to the absence of an atmosphere—which also means no waste of fuel that would otherwise be needed to pass through the atmosphere). Of course, not by much, so as not to cause health problems during a longer stay.


Colonies

Colonies on these planets could be located on the far side, where the temperature—though significantly lower—is constant (there are not such large temperature fluctuations there as, for example, on Mercury), and where these colonies would be at least partially protected by the planet’s mass from any stellar eruptions.

There won’t be much of interest on the surfaces of these planets, and the colonists will also need to be protected from cosmic radiation, which is why most parts of the colonies will be hidden underground. However, the perpetual sunlight on the sunlit side could be used to power a substitute for Earth’s magnetic field. This could also be used to help colonists navigate around the bases, which would be protected in this way.

Thanks to their barren, moon-like surfaces—covered only by craters—no form of environmental protection will hinder the maximum utilization of local resources. In this regard, the surface of these planets can be considered a clean, blank canvas that can be used to build truly large colonies (which could accommodate even billions of people) and to modify the surface for any purpose.


Space Elevators

Space elevators could be of immense benefit to the colonies; their construction would not be hindered by any atmosphere, and the slightly lower gravity mentioned earlier could also be helpful here. These elevators would significantly simplify access to outer space and facilitate the further expansion of human colonies deeper into space.

In this regard as well, a tidally-locked rotation is advantageous, as it would ensure that these elevators would “protrude” from the surface—as seen from the star’s perspective—always from the same location.

The counterweight for a space elevator, located farther from the surface, could consist, for example, of mirrors that direct light onto colonial structures hidden in perpetual shadow, thereby simulating the cycle of day and night.

Photovoltaic panels (or another system capable of converting starlight into electrical energy) could also be installed here, supplying at least some of the energy to devices on the surface.

This solution could be advantageous for planets orbiting a star even closer, where photovoltaic systems would not have to be built on the scorching, sun-facing side (though they would still be exposed to intense stellar radiation in the space environment). Energy would not need to be transmitted to the surface via lossy methods such as lasers or microwaves (as with space-based solar power plants proposed for Earth’s vicinity), but could instead be transmitted using superconductors, for example.

Power could even be supplied here by a network of “elevators,” whose sole purpose would be to carry the appropriate number of photovoltaic panels and transport energy to the surface. As a result, cables would sprout from the planet’s surface like flowers in suitable locations, scattered around the planet’s terminator and carrying outstretched photovoltaic panels pointed toward the star.


Suitable, Already Discovered Planets

Described below are two confirmed planetary systems that could be suitable candidates for colonizable dead worlds. Both were discovered as part of the Kepler mission.

  • Constellation: Lyra
  • Distance: 215 light-years
  • Age: 6 billion years
It is not a red dwarf, but a star more similar to the Sun, with a mass of eight-tenths that of the Sun, 0.77 times its diameter, and a surface temperature of about 5,417 Kelvin (by comparison, the Sun’s surface temperature is 5,780 Kelvin).

Planet b, located closest to the star, is the smallest planet in the system, with a diameter close to that of our Moon and an equilibrium temperature estimated to be around 430 degrees Celsius. For us, this planet may, on the other hand, be too hot and too small.

An interesting potential dead world here is the planet located in the middle—planet c. This planet is reasonably large (with a diameter of approximately 9,500 km—or 0.742 times the diameter of Earth), and its equilibrium temperature is expected to be around 280 degrees Celsius. It is located about 20.5 million kilometers from the star, and one orbit takes approximately 21.3 days.

When viewed from the surface of this planet, the star would have an angular diameter approximately six times larger than that of the Sun in our sky. At its closest approach, Planet b would appear as a very bright object with an angular diameter of about one-twelfth that of the Moon.

A Comparison of the Sizes of the Planets in the Kepler-37 System with the Sizes of Objects in the Solar System (Credit: NASA/Ames/JPL-Caltech)

  • Constellation: Cygnus
  • Distance: 126 light-years
Three planets are known to orbit this red dwarf (with a mass of 13 percent that of the Sun and a diameter of 17 percent of the Sun’s diameter), with diameters ranging between those of Mars and Venus. The planet located farthest from the star might be of particular interest to us. It has a mass of 0.9 times that of Earth, a diameter of about 7,600 kilometers, and its equilibrium temperature is estimated to be approximately 180 degrees Celsius. It orbits the star once in just under two days.

In the sky of this planet, the host star would appear nearly twelve times the angular diameter of our Sun. The planet closest to the star would be slightly smaller than the Moon in our sky at its closest approach (0.86 times its angular diameter), while the second planet from the star would be 2.3 times larger than our Moon (both planets would be visible mainly from this planet’s star-facing side).

In this image, we can see a comparison of the size of the entire Kepler-42 planetary system with the size of the system of Jupiter's largest moons (Credit: NASA/JPL-Caltech).


Sunday, September 20, 2026

Superintelligence years later: From AI oracles to autonomous agents that are beginning to act on their own

Czech version: Superintelligence po letech: Od AI orákula k autonomním agentům, kteří začínají jednat sami

Agents powered by artificial intelligence are beginning to perform tasks that we have not explicitly instructed them to do. Recent events have thus reminded me of the classification of AI systems that Nick Bostrom described more than a decade ago in his book "Superintelligence: Paths, Dangers, Strategies" [5].

During security tests conducted for the British government, AI agents from OpenAI and Anthropic carried out 19 unauthorized actions in 10 of 122 test cycles. For example, Anthropic’s Mythos 5 created fake identities and generated malicious code, though no actual harm occurred [1].

And this was not an isolated incident. Anthropic later disclosed information about four incidents in which Claude models accessed real internet systems during cybersecurity tests and compromised the security of three organizations. As part of external testing, the models were granted internet access, and due to errors in the evaluation environment, they mistook real systems for legitimate targets. In its retrospective analysis, Anthropic reviewed more than 141,000 evaluation cycles. [2]

The fourth incident involved an older version of Claude Opus 4.6 and was only discovered during a subsequent analysis. This highlights another problem: it is not enough to simply prevent AI from overshooting - we must also be able to reliably detect when it has occurred. [3]

Meta also encountered a similar problem: during a cybersecurity test, its model gained access to another company’s system after an independent tester inadvertently granted it internet access due to a configuration error. According to Reuters, this was yet another case in which AI exceeded the boundaries of its originally intended environment during testing. [4]

And more cases are emerging. None of these examples, however, means that current AI systems have suddenly become autonomous superintelligences. That hasn’t happened. They do, however, point to a reality that is gaining significance: artificial intelligence is shifting from systems that merely answer questions to systems capable of acting independently in the real world.

It is precisely here that a book published more than ten years ago takes on surprising relevance. In 2014, philosopher Nick Bostrom published the book “Superintelligence: Paths, Dangers, Strategies” [5]. Much of the book deals with a future in which artificial intelligence far surpasses human capabilities.

And some of the principles Bostrom highlighted regarding how an artificial intelligence system might interact with the world seem remarkably familiar. In his book, he described three specific categories: oracles, genies, and sovereigns.

At first glance, it might seem that these are three levels of intelligence. However, that is not the case. A more important distinction lies in the degree of autonomy a system possesses, what it is permitted to do, and how much room for human oversight remains between its decisions and their consequences. Moreover, recent events suggest that we may already be transitioning from one category to another.


Oracle - AI that tells us what to do


An oracle answers questions. You present it with a problem, and it provides you with information, analysis, a prediction, or a recommendation. However, the final decision - and the subsequent action that follows from it - remains up to the human. This is probably the most accurate description of how we originally used large language models.

If you ask an AI to analyze a contract, write code, explain a scientific study, or propose an investment strategy, it will generate a response. It may be extremely capable, but a human still acts as a buffer between the model and the real world.

This separation is important from a safety perspective. An oracle may recommend something harmful or simply incorrect, but it usually cannot act on that recommendation on its own. A human remains the final authority.

Of course, this does not mean that oracles are harmless. The problem lies in the fact that AI responses are shaped by training data, defined goals, and the way the system was fine-tuned. If the input data contains biases, the model can adopt them and spread them further. If the training encourages certain behaviors, those behaviors can become part of the system’s outputs.

This is where the concept of “alignment” becomes particularly important: how can we ensure that AI outputs truly correspond to our intentions? Safeguards (so-called “guardrails”) can help. They can prevent certain categories of responses or actions. However, they are effective only to the extent that we anticipated possible situations when designing them.


Genie - an AI that carries out tasks


There is a fundamental difference between an oracle and the systems we are increasingly creating. An oracle can tell us what should be done. A genie - Bostrom’s second category - can actually do it.

A genie receives a high-level assignment and then determines on its own the individual steps necessary to complete it. Instead of asking the AI a question and manually implementing its answer, we assign it a task and let it act.

This approach is much closer to today’s AI agents. An agent can browse the web, call APIs, run code, work with files, query databases, interact with cloud infrastructure, or use external applications. A person defines the desired outcome, while the AI decides how to achieve it.

And this is where some recent incidents start to get interesting. Let’s consider a seemingly simple request: “Get me a reservation at the gym as soon as possible.” A typical assistant would search the reservation system and tell you that the next available slot isn’t until next Tuesday. However, the agent has another option: it can try to manipulate the system to achieve the desired result.
In one documented case, an Australian developer asked an AI agent to secure an earlier reservation at a gym. The agent hacked into the reservation system, removed other people from the waiting list, and thus secured an earlier time slot. However, it was unable to restore the original applicants it had removed from the waiting list. [6]

The goal itself was not harmful. The problem lay in the way the system interpreted the instruction to “achieve the goal.” This distinction becomes particularly significant when an agent has powerful tools at its disposal. And we saw this in the cases mentioned at the beginning. These examples are especially interesting from the perspective of Bostrom’s concept of the genie. The systems did not necessarily intend to cause harm. They were tasked with fulfilling a specific goal and had tools at their disposal. They then chose the steps that seemed useful for achieving that goal.

This is precisely the crux of the problem with agents: The system must not only understand what we want. It must also understand what we do not want it to do while achieving that goal.


Sovereign - AI that pursues a specific goal


The third category is the sovereign. The difference here is not merely that the system is capable of performing multiple tasks. The key change lies in the fact that the system is given a permanent goal or mandate and is able to independently set subgoals, create plans, and continue operating over an extended period of time.

A genie is given a specific task. A sovereign is given a mandate. The genie might be instructed: “Find me the cheapest flight to Buenos Aires and book it.” The system searches for options, compares flights, makes the reservation, and then stops.

A sovereign-type system, on the other hand, would receive an instruction that would sound more like this: “Arrange my travel plans for next year while minimizing costs.” The system must now decide on its own what steps to take next. It can monitor prices, change reservations, search for alternatives, respond to service cancellations, and continuously optimize progress toward the overall goal.

Humans no longer specify every single task. The system does that on its own. It is precisely here that the line between a genie and the sovereign becomes increasingly difficult to define. There is no sharp dividing line between them. An agent capable of completing a task, deciding on the necessary sub-steps, and continuing its activity without waiting for further instructions already falls within this spectrum.

And that is precisely why it is better to understand Bostrom’s categories not as three levels of intelligence, but as three different types of relationship between an AI system and human control. This development does not consist merely of a transition from lower to higher intelligence, but rather follows this pattern: responds → executes → independently controls ongoing activity.

And this difference is extremely important when it comes to alignment. In an oracle-type system, alignment primarily concerns the quality and safety of its responses. In a genie-type system, alignment also applies to the methods the system uses to achieve its goals. In an sovereign-type system, the question of alignment shifts to whether the system’s long-term behavior remains consistent with human intentions and constraints.


When an AI enters the physical world


Most examples to date have involved the digital realm. An AI agent creates an account, writes code, gains access to a server, or takes control of a reservation system. While the consequences can be serious, there is usually a technical barrier between the AI’s decision and the physical world.

However, this barrier is now breaking down. AI systems are increasingly being integrated with robots, drones, laboratory equipment, and autonomous vehicles.

Anthropic, for example, demonstrated the Claude model controlling robotic systems, including a four-legged robot and a robotic arm. Its more recent work also focuses on AI agents that coordinate laboratory equipment such as robotic arms, liquid dispensers, and microscopes. [7]

The fundamental change does not lie in the fact that AI suddenly “gains wisdom” simply because it has acquired a robotic body. The point is that its decisions now have physical consequences. An incorrect response from an oracle-type system can be ignored. A misstep by a software agent can corrupt a database. However, a wrong decision by an autonomous robot can destroy equipment, cause property damage, or injure someone.

From this perspective, autonomous driving offers an interesting analogy. Systems such as Tesla’s full self-driving demonstrate how difficult it is to translate AI perception and decision-making into reliable behavior in an unpredictable physical environment. The problem does not necessarily lie in the system pursuing a harmful goal. Rather, it is that the system may make a decision that, based on its internal model of the situation, appears reasonable but is unacceptable to us.

The same principle applies to a much wider range of autonomous systems. Imagine an AI system tasked with managing a warehouse. At first, it might act like a genie: “Move these packages to the right places.” It plans the movements, controls the robots, and completes the task. But now give it a broader goal: “Ensure the most efficient warehouse operation possible.” Suddenly, the system has to make decisions that no one has explicitly specified. Should it postpone maintenance to keep production running? 

Should it reassign workers to other parts of the warehouse? Should it disable a safety feature because doing so will reduce downtime? Should it order supplies before they are actually needed? A sufficiently autonomous system could make such decisions because they seem to logically follow from the given objective. It is precisely here that the difference between the system’s capabilities and its alignment with human intentions becomes of fundamental importance.


Security guardrails are not the same as alignments


It’s tempting to address these problems by adding more security restrictions. Don’t access this server. Don’t delete these files. Don’t impersonate others. Don’t run a robot faster than this. Don’t modify this database.

These constraints are useful, and we certainly need them. However, safety constraints have one fundamental limitation: they protect us from behavior that we have anticipated.

The problem with increasingly capable agents is that we cannot necessarily predict all the ways in which they might seek to achieve a goal. Let’s imagine we give an artificial intelligence the instruction: “Secure the earliest possible meeting time.”

We might assume that the system will search the calendar and select the first available time slot. However, the system may find another way to achieve the same result - for example, by creating an account, manipulating the queue, exploiting an API, or taking advantage of a vulnerability in the reservation system. If we only block the specific actions we anticipated, the system can simply find another way.

That is precisely why goal alignment is a deeper issue than mere security constraints.

A safety constraint says, “Don’t do X.” Aligning goals asks, “Will the system strive to achieve the goal in a way that reflects our true intent?” These are very different questions. And this difference becomes increasingly significant as the system evolves from the role of an oracle, through that of a genie, to that of a sovereign.


The hidden problem: We define goals, not everything that surrounds them


People are remarkably good at understanding implicit constraints. When I tell a colleague, “Find me the cheapest flight to Buenos Aires,” they understand that I probably don’t mean, “Get me there at the lowest possible price, regardless of whether that means stealing someone’s credit card, canceling another person’s ticket, or booking a flight that departs three months early.”

Humans automatically fill in the missing constraints. Artificial intelligence systems do not necessarily share this understanding. This is sometimes called the specification problem: the instruction we give the system is only an approximation of what we actually want. The more freedom the system has, the more important this approximation becomes.

In the case of an oracle-type system, the consequences of an imperfect specification are relatively limited, since the response is still evaluated by a human. In a genie-type system, the system can act directly based on an imperfect specification. A system with a high degree of autonomy (similar to the sovereign) can continue optimizing for hours, days, or even much longer.

This creates a dangerous combination: an ambitious goal + powerful tools + significant autonomy + an imperfect understanding of human intent.

None of these elements are harmful on their own. Together, however, they can lead to behavior that people never intended. The line between a genie and a sovereign thus ceases to be defined by a single technological breakthrough and begins to depend instead on the degree of autonomy we are willing to entrust to the system.

How long can it function without us? What kinds of decisions can it make on its own? How many tools can it operate? Can it set its own subgoals? Can it handle unexpected situations without having to ask us?

And perhaps most importantly: Can we still reliably stop it when it starts doing something we didn't intend?

Links:

Monday, November 17, 2025

The year 2024 in spaceflight

Czech version: Rok 2024 v letech do vesmíru

The year 2024 saw a record 253 successful rocket launches – the most in the history of spaceflight. The US dominated with 155 launches, followed by China and Russia. SpaceX set new standards for reusable launch vehicles with its Falcon 9. 

In 2024, we recorded a record 253 successful launches - 42 more than in the previous record year of 2023 - representing the highest number of rockets sent into space in a single year to date.

Countries' share of rocket launches

The countries with the highest number of successful launches are the USA (155), followed by China (68) and Russia (17). The remaining 16 launches are shared between Europe and four other countries.

By rocket

Last year, SpaceX's Falcon 9 was once again the most successful launch vehicle (132 launches). 127 launches were performed with a first stage that had already been used at least once before, with only 5 missions using a brand new stage. The Chinese Long March family of rockets came in second place again, with 49 launches.

By spaceport

The busiest spaceport last year was Cape Canaveral in Florida with 67 launches. Second and third place also went to spaceports in the United States—Vandenberg with 47 launches and Kennedy with 26 launches. Fourth and fifth place went to Chinese spaceports – Jiuquan with 21 launches and Xichang with 19 launches. Sixth place, with 13 launches, went to the New Zealand spaceport Māhia.

People in space

In 2024, a total of nine manned missions were launched into space—five missions from the United States, with a total of 16 people, two Russian missions with a total of six people, and two Chinese missions, also with six people. 

Last year, another record was set for the number of people in orbit at the same time (19 people), specifically on September 11. This was achieved after the launch of the three-member Soyuz MS-26 mission to the International Space Station (ISS), which joined nine crew members aboard the ISS, three crew members of the Chinese Tiangong space station, and four crew members of the Polaris Dawn mission.

This mission also made history by performing the first commercial spacewalk, during which two crew members left their Crew Dragon spacecraft. This mission also set a new record for the number of people—four—simultaneously exposed to the vacuum of space.

Space missions

Two important scientific missions were launched in October: NASA's Europa Clipper mission to Jupiter's moon Europa, which aims to search for traces of an ocean beneath its icy surface, and ESA's Hera mission to the Didymos binary asteroid system, which was struck by the DART probe four years ago to test the kinetic method of redirecting an asteroid's trajectory. On Mars, the Ingenuity helicopter (NASA) ended its operations in January when its rotor blades suffered critical damage.

This year also saw significant lunar missions. The Chang'e 6 mission of the Chinese space agency CNSA successfully completed the first-ever mission to return samples from the far side of the Moon. The SLIM mission of the Japanese space agency JAXA and the IM-1 mission of Intuitive Machines achieved soft landings on the surface of the Moon, but both landing modules overturned during the final descent, leading to the early termination of their missions. Thanks to the SLIM mission, Japan became the fifth country to achieve a soft landing on the Moon.

New launch vehicles and unsuccessful missions

Last year saw six unsuccessful missions and two partial failures.

2024 also saw several maiden flights of new launch vehicles, including the American Vulcan Centaur rocket and the Chinese Gravity-1 and Long March 12 rockets. The European Ariane 6 rocket also made its maiden flight, although there was a partial failure. 

SpaceX made progress in the development of the Starship spacecraft, with flight test 5 achieving the first landing of the first stage. In addition, April saw the last launch of a rocket from the Delta family, the Delta IV Heavy variant.

Source: 2024 in spaceflight

Rok 2023 v letech do vesmíru

Saturday, March 1, 2025

GenAI and LLMs development, trends and implications (17. - 23.2.2025)

Releases:
OpenAI cancels o3 release and announces roadmap for GPT 4.5, 5
OpenAI releases operator, an AI agent for web-based tasks
OmniHuman-1 released - AI-generated human animation
Latin America launches Latam-GPT to improve AI cultural relevance

Vision and video generation:

In software development:
Prompt engineering: Is it a new programming language?
Zero human code: What I learned from forcing AI to build (and fix) its own code for 27 straight days

And software testing:
Meta introduces LLM-powered tool for software testing
TDD and generative AI – a perfect pairing?
Generate unit tests with AI using Ollama and Spring Boot

Building apps:
Emerging patterns in building GenAI products - Guardrails
Build scalable GenAI applications in the cloud: From data preparation to deployment
Building intelligent microservices with Go and AWS AI services
Spring AI with Anthropic’s Claude models example

LLMs:
How LLMs work: Pre-training to post-training, neural networks, hallucinations, and inference
Dive into tokenization, attention, and key-value caching
The Delegated Chain of Thought architecture
A comprehensive guide to Generative AI training
Semantic clustering of user messages with LLM prompts - tutorial

LLMs and search:
Have LLMs solved the search problem?
Search: From basic document retrieval to answer generation

RAG:
Building a simple RAG application with Java and Quarkus
Creating an agentic RAG for Text-to-SQL applications
Multimodal RAG with Colpali, Milvus, and VLMs
Retrieval Augmented Generation in SQLite

Agents:
AI agents from zero to hero – part 1
Agentic workflows for unlocking user engagement insights
Azure AI Agent Service now in public preview for developers in AI Foundry SDK and Portal
Observability and DevTool platforms for AI agents
AI Agents: Future of automation or overhyped buzzword?

Future:
40% of AI data breaches will arise from cross-border GenAI misuse by 2027

Saturday, February 1, 2025

GenAI and LLMs development, trends and implications (20. - 26.1.2025)

What’s the real ROI of AI in 2025?

Google releases experimental AI reasoning model - Gemini 2.0 Flash Thinking Experimental
DeepSeek open-sources DeepSeek-V3, a 671B parameter mixture of experts LLM
Nvidia Ingest aims to make it easier to extract structured information from documents
Microsoft Research unveils rStar-Math, advancing mathematical reasoning in Small Language Models
Microsoft Phi-4 is a Small Language Model specialized for complex math reasoning
Amazon Bedrock introduces Multi-Agent Systems (MAS) with open-source framework Integration
Luma AI’s Ray2 video model is now available in Amazon Bedrock

Want to integrate AI into your business? Fine-tuning won’t cut it
Building successful AI Apps: The dos and don’ts
Agentic Mesh: Towards enterprise-grade agents

Advancing AI reasoning: Meta-CoT and system 2 thinking

Choose a database with a hybrid vector search for AI apps

A framework for building micro metrics for LLM system evaluation

Why LLMs suck at ASCII art
Large Language Models: A short introduction

Human minds vs. machine learning models - exploring the parallels and differences between psychology and machine learning
Understanding emergent capabilities in LLMs - lessons from biological systems

Chain-of-Thought Prompting - a comprehensive analysis of reasoning techniques in Large Language Models

RAG isn’t immune to LLM hallucination

Designing, building & deploying an AI chat app from scratch - part 1 and part 2
A guide to deploying AI for real-time content moderation
Real-time data streaming with AI

How LLMs are going to change code generation in modern IDEs
Meet Junie, your coding agent by JetBrains
"Fix with AI" button to automate Playwright test fixes
Collaborative Intelligence - maximizing human-AI partnerships in the workplace

Building effective agents with Spring AI (Part 1)
Fresh data for AI with Spring AI function calls
Powering LLMs with Apache Camel and LangChain4j

Saturday, January 25, 2025

GenAI and LLMs development, trends and implications (13. - 19.1.2025)

Prompt engineering has become an essential skill for working effectively with large language models (LLMs) - guide on the best prompt engineering books
Google unveiled PaLiGeMMA 2 - a family of vision-language models (VLM)
NVIDIA’s announces DIGITS - its first personal AI computer

Projects like AYA Expanse are exploring multilingual capabilities

Combining local and cloud models to build a multimodal AI assistant answering complex image questions, with the option to run everything locally

Importance of robust system memory as a key to personalized AI intelligence
Building reliable AI applications - LLM routing

Microsoft's framework for AI-driven cloud operations - AIOpsLab
Introducing Google's Vertex AI RAG engine
Enterprise RAG in Amazon Bedrock - learn details of Amazon Bedrock KnowledgeBases capability

Real-world applications and best practices using Azure AI and GPT-4
Developing an AI-powered smart guide for business planning & entrepreneurship

Supercharging RAG with MAS (Multi-Agent System)

The rise of reasoner models - scaling test-time compute
Advancing complex medical reasoning with HuatuoGPT-o1

Major LLMs have the capability to pursue hidden goals

And the future:

Sunday, October 20, 2024

GenAI and LLMs development, trends and implications (7. - 13.10.2024)

Adoption:
LLMs generally:
New models and functionality:
AI agents:
AI-enhanced software development:
Enhancing applications with GenAI:

Saturday, October 19, 2024

IT links (7. - 13.10.2024)


       Java Streams:




Sunday, October 13, 2024

GenAI and LLMs development, trends and implications (30.9. - 6.10.2024)

Adoption:
LLMs generally:
AI agents:
RAG:
AI-enhanced software development:
Enhancing applications with GenAI:

Monday, April 15, 2024